Multimedia Information Retrieval- A Review - IJRTER
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Multimedia Information Retrieval- A Review
Mrs.Vrushali A. Patil1, Dr. Mahesh kumbhar2
1Department of Electronics, R.I.T.,Sakharale,India
2 Department of Electronics, R.I.T.Sakhrale,India
Abstract- Traditionally, the database is of text and numerical data only, which is having less
attention nowadays because of the massive amount of multimedia content. In the multimedia and
storage technology, the preceding two decades have resulted in a substantial progress that has led to
building of a large repository of digital image, video, and audio data. Currently, the information
retrieval from the multimedia content is having a great attention. For exploring through the myriad
variety of media all over the world Content-based multimedia information retrieval provides
paradigms and methods. In this paper, an extensive review on Multimedia content based retrieval is
presented together with the classification by means of the multimedia content either it may be an
image or audio or video. In addition, a concise description about multimedia content retrieval is
presented. Further more, a brief description on image, audio and video retrieval is presented.
Keywords- Multimedia Information, Content Based Retrieval, Content Based Image Retrieval
(CBIR), Content Based Audio Retrieval (CBAR), Content Based Video Retrieval (CBVR),
segmentation
I. INTRODUCTION
Multimedia is “the combination of different digital media types, such as, Video (MPEG, MOV,
WMV etc.), Audio (MP3, OGG, MIDI etc.), Image (JPEG, GIF, PNG) and Documents (PPT, PDF,
TXT etc.) into an integrated multisensory interactive application or presentation to convey a message
or information to an audience.”[1,2]. The amount and volume of multimedia data, such as television
broadcasts and surveillance videos are quickly increased with the advances in information
technologies [3].
Content-based retrieval is an approach for accessing data in multimedia databases [4. Content- based
methods can prospectively improve retrieval accuracy even when text annotations are present by
providing additional insight into the media collections [5].
In recent years, a wide range of researches are carried out in the information retrieval by a huge
number of researchers. The multimedia contents image, audio and video plays a vital role because of
its utilization. Hence in this paper, an extensive review of extremely important researches on
multimedia content based retrieval is presented. The significant researches are classified according to
the multimedia contents image, audio and video with their processing and analyzing methods. The
remaining sections of the paper are organized as follows. In section 2 a short description on Content
based Image Retrieval is presented and in section 3 details the introduction on Content based Audio
Retrieval. In section 4 a concise description on Content based Video Retrieval (CBVR) is presented.
A broad review of multimedia content based retrieval is presented in the section 5. A brief description
on the directions for the future research methods are presented in the section 6and section 7 concludes
the paper.
II. CONTENT BASED IMAGE RETRIEVAL (CBIR)
The process of surfing, searching and recovering images from a huge database of digital images is
named as Image retrieval [6]. Primary image retrieval methods were based on text that associated
textual information, similar to filename, captions and keywords with every image in the repository
@IJRTER-2016, All Rights Reserved 191International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
[7]. However, traditional text-based (keyword-based) image retrieval techniques do not work well
as the image contents cannot be exactly illustrated by human language and different persons
may recognize the same image in a different way [8].
Content-based image retrieval (CBIR), encompassing its origin from image processing, computer
vision, extremely huge databases and human computer interaction is one of the vital research topics
[9]. The schematic of CBIR system is as shown in figure 1.
Figure 1 CBIR system
Applications in internet, crime detection, entertainment, medicine, marketing and digital libraries
are found by Content based image retrieval (CBIR). The preferred characteristics of CBIR system are
high retrieval efficiency and less computational complexity and they are the key purpose in the design
of CBIR system [10].
III. CONTENT BASED AUDIO RETRIEVAL (CBAR)
With the rapid enlargement of social media, huge amounts of sound substance are becoming
available through the web every day. A very active online community has contributed sounds that
have been a vital factor for the rapid increase in the number of sounds available [12]. In the case of
the digital music industry, millions of tracks enclosed by current major Internet stores that make
difficult for search, retrieval, and detection of music applicable for a user [13]. An essential part of
numerous modern computer and multimedia application is Audio Data [14]. By tradition, musical
information has been retrieved and/or classified based on standard reference information, such as the
name of the composer, the title of the work, the album title, the style of music, and so on. Efficient
and accurate automatic music information processing (in particular, accessing and retrieval) will be
tremendously important research issues of this century [15].
Content Based Audio Retrieval System (CBARS) refers to penetrating for audio data in a database to
retrieve a small number of audio files that holds similarity to the user input [16]. It tolerate users to
illuminate the query as what they want, so it constructs query formulation more comprehensive and
easier than key word based retrieval [17]. i.e., a short excerpt of music, called the pattern, is searched
for in a superior body of music, named as the score [18].
In recent years, assuring content-based audio retrieval methods that might be classified into two main
paradigms have emerged. In the initial paradigm, the ‘Query by humming’ (QBH) approach is
attempted for music retrievals [19]. However, this approach has the drawback of being possible only
when the audio data is music stored in some representative format or polyphonic transcription (i.e.
MIDI) . Furthermore, it is not appropriate for various music genres such as Trance, Hard-Rock,
Techno and many others. Such a constrained approach evidently cannot be a standard solution for the
audio retrieval problem [20]. The succeeding paradigm is named as “Query-by-Example” (QBE), a
reference audio file is utilized as the query and audio files with identical content are returned and
ranked by their similarity. In order to search and retrieve universal audio signals such as the raw
@IJRTER-2016, All Rights Reserved 192International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
audio files (e.g. mp3, wave, etc.) on the web or databases, only the QBE model is currently
applicable [21]
IV. CONTENT BASED VIDEO RETRIEVAL (CBVR)
Most of the principles, ideas, methods and algorithms developed for content-based image retrieval
can be expanded to video retrieval, but such expansions are not as easy as offering to each video
frame the same treatment one would provide to individual images [22]. Video images include a wider
variety of ancient data types (the most understandable being motion vectors) and occupy far additional
storage than still images. For still images they can take hours to review compared to a few seconds
[23]. To execute queries on every video frame, it is very ineffective and time consuming. In addition,
large amount of video databases are frequently positioned on distributed platforms, impressing huge
transmission bandwidth requirements [24]. For the increasing multimedia applications, such as
video browsing, content-based indexing and retrieval, the linear representation of video sequences
is also not sufficient. For this reason, for extracting a little but meaningful amount of the video
information a content-based sampling algorithm is typically applied to video data [25].
Retrieval is achieved by matching the feature attributes of the query object with those of videos in the
database that are adjacent to the query object in high dimensional spaces [26]. The query-based
video database access approaches usually require that users give an example video or sketch, and a
database is then searched for videos, which are related to the query [27]. It helps the users in the
retrieval of favored video segments from a huge video database efficiently based on the video
contents with the aid of user interactions [28]. For example, a user analyzing a soccer video will
request for specific events such as objectives [29].
V. AN EXTENSIVE REVIEW ON MULTIMEDIA CONTENT BASED RETRIEVAL
A range of research methodologies employed for developing the multimedia content based retrieval
is presented in this section. The reviewed models are classified and described in the following
subsections.
A. Content Based Image Retrieval (CBIR)
The exploitation of visual information on the World Wide Web has become more familiar, with the
rapid development of the Internet and computer technology. There has been a tendency in retrieving
images on the basis of automatically-derived features such as color and shape. In the field of
Information Technology, conducting the image retrieval by query image has attracted plenty of
attention from researchers. The CBIR (Content Based Image Retrieval) technique refers to the image
retrieval operation which is based on the key primitives. Despite the fact that in many application
domains, the CBIR technique has been accepted and implemented, extracting images of their
contents is really a strenuous job. The complicatedness is determining how to organize these image
features in some fashion without consuming excessive time. A MOT (Mesh of Trees) based CBIR
system has been proposed by Wei-Min Jeng et al [30]. To afford more efficiently better query
results, it makes use of dual image signatures.
Srinivasa Rao et al. [31] have proposed Content Based Image Retrieval (CBIR) system using
Contour let Transform (CT) based features with elevated retrieval rate and less computational
complexity. For
feature extraction of images in the database, unique properties of CT like directionality and
anisotropy made it an authoritative tool. Over the work based on Gabor-Zernike features based CBIR
system, enhanced outcomes in terms of computational complexity and retrieval efficiency are
observed. In the proposed CBIR system, the distance measures viz., Manhattan distance and
@IJRTER-2016, All Rights Reserved 193International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
Euclidean distance are utilized as similarity measures. In terms of average retrieval rate, superiority
of Manhattan distance is scrutinized over Euclidean distance.
In content based image retrieval systems, Color, texture and shape information have been the
primitive image descriptors. For combining all the three i.e. color, texture and shape information,
Hiremath et al [32] have presented a framework and achieved higher retrieval efficiency using image
and its complement. The image and its complement were partitioned into non-overlapping tiles of
equivalent size. The features drawn from conditional co-occurrence histograms among the image
tiles and equivalent complement tiles, in RGB color space, hand out as local descriptors of color and
texture. This local information was captured for two resolutions and two grid layouts that afford
diverse details of the similar image. An integrated matching scheme, based on Most Similar Highest
Priority (MSHP) principle and the adjacency matrix of a bipartite graph formed using the tiles of
query and target image, has been afforded for matching the images. Using Gradient Vector Flow
fields, Shape information has been captured in terms of computed edge images. Later, Invariant
moments are utilized to record the shape features. The combination of the color and texture features
among image and its complement in conjunction with the shape features afford a robust feature set
for image retrieval.
In the precedent few years, Content Based Image Retrieval (CBIR) has turn out to be one of the most
active research areas. Numerous indexing techniques depend on global features distribution such as
Gabor Wavelets. By means of an emerging technique recognized as Independent Component
Analysis (ICA), Arti Khaparde et al [33] have proposed an approach for global feature extraction. In
a databank, a relative study was done between ICA feature vectors and Gabor feature vectors, for 180
diverse texture and natural image. In terms of retrieval accuracy, computational complexity and
storage space of feature vectors as compared to Gabor approaches, result analysis have been exposed
that extracting color and texture information by ICA affords significantly enhanced outcomes.
Content-Based Image Retrieval (CBIR) is a significant and increasingly popular approach that aids
in the retrieval of image data from a huge collection. In the last decade, Content-Based Image
Retrieval has concerned voluminous research paving way for enlargement of numerous techniques
and systems besides creating interest on fields that aid these systems. CBIR indexes the images based
on the features attained from visual content so as to assist speedy retrieval. For content-based image
indexing and retrieval, Suresh Pabboju et al [34] have presented an elegant system. For indexing the
images, the system has combined the global and regional features. The Image Processing techniques
like Color space conversion, Quantization, Denoising, Edge detection and Segmentation has been
exploited by the proposed system. To segment the images in very effective mode, the image
segmentation technique proposed has been found. R*-Tree data structure is applied in indexing the
region features. The fractional distance measures applied in the retrieval have outperformed both the
similarity measures: L1 and L2 norms. The experimental results have demonstrated that the proposed
system has been efficiently retrieve similar images from a collection of images based on a query
image in addition enhancing retrieval accuracy.
For diagnosis support in medical fields, Quellec et al [35] have proposed Content-Based
Image Retrieval (CBIR) method. In the proposed system, without extracting domain-specific
features: a signature is built for each image from its wavelet transform; images are indexed in a
generic fashion. In each sub band of the decomposition, these image signatures characterize the
distribution of wavelet coefficients. To compare two image signatures, a distance measure is then
defined and thus retrieves the majority similar images in a database when a query image is submitted
by a physician. The signatures and the distance measure must be related to the medical interpretation
of images, to retrieve relevant images from a medical database. They have introduced
numerous degrees of freedom in the system as a result it can be tuned to any pathology and
image modality. They have
proposed to adapt the wavelet basis in specific, within the lifting scheme framework, and to utilize a
custom decomposition scheme. Moreover weights are introduced between sub bands. All these
@IJRTER-2016, All Rights Reserved 194International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
parameters are tuned by an optimization procedure, by employing the medical grading of each image
in the database to define a performance measure. With the rapid development of technology, the
traditional information retrieval techniques based on keywords are not adequate, Content - Based
Image Retrieval (CBIR) has been a dynamic research topic. By using unique descriptors from a
trained image Content Based Image Retrieval (CBIR) technologies provide a method to find images
in large databases. The capacity of the system to categorize images based on the training set feature
extraction is pretty exigent. have proposed to extract features on MRI scanned brain images using
discrete cosine transform and down sample the extracted features has been propose by Ramesh Babu
Durai et al [36] by the alternate pixel sampling. To verify the efficacy of various classification
algorithms on dataset, the dataset so created was investigated using WEKA classifier. The upshot of
the results by means of DCT for feature extraction was pretty promising. Another method of image
processing content based image is said to be an opportunity of recovery Content Based Information
Retrieval (CBIR), posing Question By Image Content (QBIC) and (CBIR). This is an application of
computer vision destined to elucidate image retrieval problem. To find the required image in large
databases we find the required data or image by applying some query on the basis of content
based shapes, textures colors etc is exploited. If the capability to estimate or analyze the image
Content does not subsist, in that case search must depend upon metadata like caption or
keywords. If the query doesn’t equal the required contents then it is implemented on some
other feature of images to retrieve from the database. Jalil Abbas et al [37] have payed attention on
the Content Based image retrieval with precise domain of Text Based Image Retrieval (TBIR)
system. Content Based is visual and Text Based is semantic by comparing the above results Text
Based Image Retrieval is rapid as compared to Content Based Image Retrieval (CBIR).
An approach for Content Based Image Retrieval using Hierarchical and K-Means clustering
techniques has been presented by Murthy et al [38] where images are initially clustered into
groups having analogous color content and then the preferred group is clustered using K-
Means. Hierarchical clustering supports faster image retrieval and also permit the search for most
relevant images in large image databases. K-Means is a clustering method based on the optimization
of an overall measure of clustering quality is recognized for its efficiency in producing precise results
in image retrieval. The user can choose an image set of his choice and further refine the search by
applying K-Means technique since each cluster obtained is a unique set of similar images. Therefore
by means of hierarchical and K-Means techniques together not only facilitates the user not to
overlook the image he may oblige but also to attain precise favored image results.
B.Content Based Audio Retrieval
Conventional database systems are designed for handling textual and numerical data, and retrieving
such data is frequently based on simple comparisons of text/numerical values. However, for the
multimedia data, this simple method of retrieval is no longer sufficient, since the digitized
representation of images, video, or data itself does not suggest the reality of these media items. In
addition, with the semantic content attained by a user’s recognition composite data consisting of
heterogeneous types of data also links. Hence, by taking such intrinsic features of multimedia data
into account content-based retrieval for multimedia data is realized. Implementation of the content-
based retrieval facility is not based on a single fundamental, but is intimately related to an underlying
data model, a prior knowledge of the area of interest, and the scheme for representing queries. For
multimedia databases from the point of view of three fundamental issues, Atsuo Yoshitaka et al [39]
have surveyed studies on content-based retrieval. All over the discussion, they have assumed
databases that handle only non-textual/numerical data, such as image or video, are also in the
category of multimedia databases.
The impasse of audio information retrieval is recognizable to a person who has returned from
vacation to find an answering machine filled with messages. Many workers are finding ways to
automatically locate, index, and browse audio using advances in speech recognition and machine
@IJRTER-2016, All Rights Reserved 195International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
listening, while there is not yet an "AltaVista" for the audio data type. Advances in automatic speech
recognition, word spotting, speaker and music identification, and audio similarity with a view
towards making audio less “opaque has been presented by Jonathan Foote [40] by reviewing the
state- of-the-art in audio information retrieval. Using automatically-derived information to go beyond
the tape recorder metaphor, a special section addresses intelligent interfaces for navigating and
browsing audio and multimedia documents.
In music analysis field, the availability of techniques and methods for feature extraction and
classification has shown that researchers in this area are very concerned in automating the extraction
and classification processes. Their analyses contribute to a major breakthrough for internet users and
others, since the collections of digital songs keep increasing online. Noor A. Draman et al [41] have
elucidated the framework with significant factors that need to be considered to get higher
performance on retrieval part. They sturdily trust that their discussion throughout the paper has given
opportunities to other researchers in this area of analysis to fill the gaps, to investigate further and to
afford solutions to the recognized and unidentified problems that have yet to be exposed. In addition,
the discussion on proposed framework especially by the approach in genre classification, confidently
contributed to others.
C. Content Based Video Retrieval (CBVR)
Philippe Aigrain et al [42] have reviewed to an enormous extent of various research efforts
and techniques addressing visual content representation and content based retrieval of visual data. It
is obvious that the number of research issues and their span are rather huge and expanding rapidly
with progress in computing and communication. As a result, more and more researchers from
diverse fields are attracted and initiate to explore these issues visual data is becoming the core of
multimedia computing. Conversely, extraction and handling semantics information of visual data
remains a major bottleneck, which calls for not only more research efforts, but also most
critically right research approaches. Application oriented approach is significant to the achievement
of visual data representation and retrieval researches and avert from being too theoretical. By
working strongly focused applications, while achieving general solutions stay to long, the reviewed
research issues can be addressed in the context of well defined applications and facilitate the
applications. When they endeavor for visual content analysis and representation, it should be
pointed out again that integration of diverse information sources, such as speech, sound and text is
as significant as visual data itself in accepting and indexing visual data. Keywords and conceptual
retrieval techniques are constantly be a significant part of visual information systems.
With exploitation of digital TV, progression in real-time video decompression, simple access to the
Internet and the availability of cheap mass storage and fast graphics adaptor cards, digital video turn
out to be the next “big” media. Regrettably, automatic indexing and feature extraction from digital
video is even harder than still-image analysis. Currently, to automatic detection of scene changes,
automatic analysis of digital video is mostly restricted. A framework suitable to instantly explore the
consequences of content-based video retrieval with a high granularity of video content has been
proposed by Volker Roth [43]. To represent video contents on a high level of abstraction, the
framework utilizes semantic networks and uses time-varying sensitive regions to link objects in a
video to the knowledge base. A prototype was implemented under next step, applying the rich user-
interface capabilities of this platform to feature drag and drop queries and authoring of the video
retrieval system.
Flash™ movies are created, delivered, and viewed by over millions of users in their daily
experiences with the Internet as a prevailing web media format. Nevertheless, issues regarding the
indexing and retrieval of Flash movies are unfortunately unnoticed by the research community,
which sternly restrict the utilization of the enormously valuable Flash resource. A close examination
divulges that the intrinsic complexity of a Flash movie, including its heterogeneous media
components, its dynamic nature, and user interactivity, makes content-based Flash retrieval a host of
@IJRTER-2016, All Rights Reserved 196International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
research issues not comprehensively addressed by the existing techniques. As the initial endeavor in
this domain, Jun Yang et al [44] have proposed a generic framework termed as FLAME
(FLash Access and Management Environment) is embodying a 3-layer structure that deal with the
representation, indexing, and retrieval of Flash movies by mining and understanding of the movie
content. To verify the feasibility and effectiveness of FLAME an experimental prototype for Flash
retrieval is implemented.
Over the Internet, the growth in obtainable online video material is generally combined with user-
assigned tags or content description, which is the mechanism, used to access such video. However,
user-assigned tags have limitations for retrieval and often we want access where the content of the
video itself is directly matched against a user’s query rather than against some manually assigned
surrogate tag. On Internet-scale, Content-based video retrieval techniques are not yet adequate
scalable to permit interactive searching, but the techniques are proving robust and effective for
smaller collections. Alan F. Smeaton et al [45] have exposed three exemplar systems which
demonstrate the state of the art in interactive, content-based retrieval of video shots, and these three
are just three of the more than 20 systems widened for the 2007 iteration of the annual TRECVid
benchmarking activity. The contribution of their article is to demonstrate that retrieving from video
using content-based methods is feasible, that it works, and that there are many systems which do this,
such as the three outlined herein.
Content-based search and retrieval of video data has turn out to be a challenging and significant
issue. Video comprises numerous types of audio and visual information which are hard to extract,
combine or trade-off in common video information retrieval. This research work is the enhanced
version of preceding research with texture feature extraction. Shanmugam et al [46] have dealt
with the specific aspect of inferring their enhanced approach for content-based video retrieval from a
compilation of videos. On purpose, they have proposed a video data model that supports the
integrated utilization of a variety of approaches. To begin with, the system divides the video into a
sequence of elementary shots and extracts a minute number of representative frames from each shot
and subsequently calculates frame descriptors depending on the Motion, Edge, Color and Texture
features. Using 2-D correlation coefficient technique, the video shots are segmented. By employing
Fast Fourier transform and L2 norm distance function, Statistical approach, HSV color space
conversion and Gabor wavelets using Fast Fourier transform, the motion, edge histogram, color
histogram and texture features of the elementary video shots are extracted respectively. Using the
above approaches the elementary video shots features are extracted and stored in feature library. The
videos are retrieved in the system on the basis of a query clip. The color, edge, texture and motion
features are extracted for a query video clip and estimated against the features in the feature library.
The comparison is carried out, with the aid of Kullback- Leibler distance similarity measure. Later,
on the basis of the calculated Kullback- Leibler distance, analogous videos are retrieved from the
collection of videos.
VI. DIRECTIONS FOR THE FUTURE RESEARCH
In this review paper, various techniques utilized for the multimedia content based retrieval has been
analyzed thoroughly. The image, audio and video which are the multimedia content researches are
analyzed. In addition, among various domains such as medical image management, multimedia
libraries, document archives, art collections and more, Image databases have turned out to be
popular. Because of the utilization of image databases, it has the prospective to attain better results in
the research community. This paper will be a healthier foundation for the budding researchers in the
multimedia content based retrieval area. In future we expect copious amount of inventive brainwaves
will rise by means of our review work.
VII. CONCLUSION
Multimedia content retrieval is a rising research area that has received much attention among the
@IJRTER-2016, All Rights Reserved 197International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
researchers in the recent years. In this paper, a comprehensive review of the significant researches
and techniques in existing CBR has been scrutinized. Here the researches are categorized based on
the multimedia content either it may image or audio or video. A concise description on the
multimedia content retrieval has presented and also brief introduction on the image, audio and video
content retrieval is also presented. This review paves the way to the budding researchers to know
about the various techniques existing in the multimedia content based retrieval.
REFERENCES
1. Patti Shank, "The Value of Multimedia in Learning", Adobe’s Media Center, 2005
2. Mohib ur Rehman, Imran Ihsan, Mobin Uddin Ahmed, Nadeem Iftikhar and Muhammad Abdul Qadir,"Generic
Multimedia Database Architecture", World Academy of Science, Engineering and Technology, Vol.5,2005
3. Anthony Lee, Ruey-Wen Hong and Meng-Fang Chang, "An Approach to Content-based Video Retrieval",
IEEE International Conference on Multimedia and Expo (ICME), 2004 [12] Grimoni, Belico dos Reis and Tori, "The
Use of Multimedia in Engineering Education – An Experience", 1998
4. Michael Clausen and Frank Kurth, "Content-Based Information Retrieval By Group Theoretical Methods",
5. Michael Lew, Nicu Sebe, Chabane Djeraba and Ramesh Jain, "Content-Based Multimedia Information Retrieval:
State of the Art and Challenges", ACM Trans. Multimedia Comput. Commun. Appl., 2006
6. Avinash Tiwari and Veena Bansal, "PATSEEK: Content Based Image Retrieval System for Patent Database", 2008
7. Darshak Thakore and Trivedi, "Content based image retrieval techniques – Issues, analysis and the state of the art",
8. Krishna Mohan, Dhananjaya and Yegnanarayana, "Video Shot Segmentation Using Late Fusion Technique,"
In proceedings of Seventh International Conference on Machine Learning and Applications, pp.267-270,2008.
9. Suresh Pabboju and Venu Gopal Reddy "A Novel Approach for Content-Based Image Indexing and Retrieval
System using Global and Region Features", International Journal of Computer Science and Network Security, Vol.9,
No.2, 2009 [10] Srinivasa Rao and Srinivas Kumar, "Content Based Image Retrieval using Contourlet Sub-band
Decomposition", Proceedings of SPIT-IEEE Colloquium and International Conference,Vol.1, No.140
10. Gerard Roma, Jordi Janer, Stefan Kersten, Mattia Schirosa, Perfecto Herrera and Xavier Serra, "Ecological
acoustics perspective for content-based retrieval of environmental sounds", Eurasip Journal Of On Audio, Speech,
AndMusic Processing, 2010
11. Dmitry Bogdanov, Martin Haro, Ferdinand Fuhrmann, Emilia Gomez and Perfecto Herrera, "Content-based music
recommendation based on user preference examples", 2010
12. Lie Lu, Stan Li and Hong-Jiang Zhang, "Content-Based Audio Segmentation Using Support Vector Machines",
IEEE International Conference on Multimedia and Expo, 2001
13. Tao Li and Mitsunori Ogihara, "Content-Based Music Similarity Search And Emotion Detection", IEEE
International Conference on Acoustics, Speech, and Signal Processing, 2004
14. Yuk Ying Chung, Ka Yee Ng, Liwei Liu, Xiaoming Chen, Mohd Afizi Mohd Shukran Eric Choi, David Yu Shi and
Fang Chen, "Design of a Content Based Multimedia Retrieval System", Proceedings of the 5th WSEAS Int. Conf.
on Circuits, Systems, Electronics, Control & Signal Processing,2006
15. Hafiz Malik, "Content-Based Audio Indexing and Retrieval: an overview", Technical Report, 2003
16. Kjell Lemstrom and Anna Pienimaki, "Approaches for content-based retrieval of symbolically encoded polyphonic
music", International Conference on Music Perception and Cognition, 2006
17. Serkan Kiranyaz and Moncef Gabbouj, "A Generic Content-Based Audio Indexing And Retrieval Framework",IEE
Proceedings- Vision, Image and Signal Processing, Vol.153, No.3, 2006
18. Iftikhar Ahmad, Faouzi Alaya Cheikh, Serkan Kiranyaz and Moncef Gabbouj, "Audio-based Queries for Video
Retrieval over Java Enabled Mobile Devices", 2006
19. Ruei-Shiang Lin And Ling-Hwei Chen, "Content-Based Audio Retrieval Based On Gabor Wavelet Filtering",
International Journal of Pattern Recognition and Artificial Intelligence, Vol.19, No.6, pp.823–837, 2005
20. Shweta Ghodeswar and Meshram, "Content Based Video Retrieval", In Proceedings of ISCET, 2010
21. Fernando, Canagarajah and Bull, "Scene change detection algorithms for contentbased video indexing and
retrieval", Electronics & Communication Engineering Journal, pp.117-126, 2001
22. Yannis Avrithis, Anastasios Doulamis, Nikolaos Doulamis, and Stefanos Kollias, "A Stochastic Framework for
Optimal Key Frame Extraction from MPEG Video Databases", Computer Vision and Image Understanding,
Vol.75, No.1/2, pp.3–24, 1999
23. Anastasios Doulamis, Nikolaos Doulamis and Stefanos Kollias, "A fuzzy video content representation for video
summarization and content-based retrieval", Signal Processing, Vol.80, pp.1049-1067, 2000
24. Jianping Fan, Walid Aref, Ahmed Elmagarmid, Mohand-Said Hacid, Mirette Marzouk and Xingquan Zhuand,
"MultiView: Multilevel video content representation and retrieval", Journal of Electronic Imaging, Vol.10, No.4,
pp.895–908, 2001
25. Shanmugam and Priya Rajendran, "An Enhanced Content-Based Video Retrieval System Based On Query Clip",
International Journal of Research and Reviews in Applied Sciences, Vol.1, No.3, 2009
26. Petkovic, "Content-based video retrieval", 2000
@IJRTER-2016, All Rights Reserved 198International Journal of Recent Trends in Engineering & Research (IJRTER)
Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]
27. Geetha and Vasumathi Narayanan, "A Survey of Content-Based Video Retrieval", Journal of Computer Science,
Vol.4, No.6, pp.474-486, 2008
28. Wei-Min Jeng and Jen-Hao Hsiao, "An Efficient Content Based Image Retrieval System using the Mesh-of-Trees
Architecture", Journal of Information Science and Engineering, Vol. 21, No. 4, pp. 797-808, July 2005
29. Srinivasa rao, Srinivas kumar and Chatterji, "Content Based Image Retrieval using Contourlet Transform", ICGST
International Journal on Graphics, Vision and Image Processing (GVIP), Vol. 7, No. 3, pp. 9-15, November 2007
30. Hiremath and Jagadeesh Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using
Image and its complement", International Journal of Computer Science and Security, Vol. 1, No. 4, pp. 25-35, 2007
31. Arti Khaparde, Deekshatul, Madhavilatha, Zakira Farheen and Sandhya Kumari, "Content Based Image Retrieval
Using Independent Component Analysis", International Journal of Computer Science and Network Security,
Vol. 8, No.4, pp. 327-332, April 2008
32. Suresh Pabboju and Venu Gopal Reddy "A Novel Approach for Content-Based Image Indexing and Retrieval
System using Global and Region Features", International Journal of Computer Science and Network Security,
Vol.9, No.2, 2009
33. Quellec, Lamard, Cazuguel, Cochener and Roux, "Wavelet optimization for content-based image retrieval in
medical databases", Medical Image Analysis, Vol. 14, No. 2, pp. 227-241, April 2010
34. Ramesh Babu Durai and Duraisamy, "A Generic Approach to Content Based Image Retrieval Using DCT and
Classification Techniques", International Journal on Computer Science and Engineering, Vol. 2, No. 6, pp. 2022 -
2024, September 2010
35. Jalil Abbas, Salman Qadri, Muhammad Idrees, Sarfraz Awan and Naeem Akhtar Khan, "Frame Work for Content
36. Based Image Retrieval (Textual Based) System", Journal of American Science, Vol. 6, No. 9, pp. 704 -707,
September 2010
37. Murthy, Vamsidhar, Swarup Kumar And Sankara Rao, "Content Based Image Retrieval using Hierarchical and K-
Means Clustering Techniques", International Journal of Engineering Science and Technology, Vol.2, No.3, pp.209 -
212,2010
38. Atsuo Yoshitaka and Tadao Ichikawa, "A Survey on Content-Based Retrieval for Multimedia Databases", IEEE
Transactions on Knowledge and Data Engineering, Vol. 11, No. 1, pp. 81-93, January 1999
39. Jonathan Foote, "An overview of audio information retrieval", Multimedia Systems - Special issue on audio and
multimedia, Vol. 7, No. 1, pp. 1-18, January 1999
40. Noor A. Draman, Campbell Wilson and Sea Ling, "Bio-inspired Audio Content-Based Retrieval Framework (B-
ACRF)", World Academy of Science, Engineering and Technology, No. 53, pp.791-796, May 2009
41. Philippe Aigrain, HongJiang Zhang and Dragutin Petkovic, "Content-based Representation and Retrieval of Visual
Media: A State-of-the-Art Review", Multimedia Tools and Applications, Vol. 3, No. 3, pp. 179-202, 1996
42. Volker Roth, "Content-based retrieval from digital video", Image and Vision Computing, special issue on Content-
Based Image Indexing and Retrieval, Vol. 17, No. 7, pp. 531-540, May 1999.
43. Jun Yang, Qing Li, Liu Wenyin and Yueting Zhuang, "Content-based Retrieval of Flash™ Movies: Research Issues,
Generic Framework, and Future Directions", Multimedia Tools and Applications, Vol. 34, No. 1, November 2006
44. Alan F. Smeaton, Peter Wilkins, Marcel Worring, Ork de Rooij, Tat-Seng Chua and Huanbo Luan, "Content-Based
Video Retrieval: Three Example Systems from TRECVid", International Journal of Imaging Systems and
Technology - Multimedia Information Retrieval , Vol. 18, No. 2-3, pp. 195-201, 2008
45. Shanmugam and Priya Rajendran, "An Enhanced Content-Based Video Retrieval System Based On Query Clip",
International Journal of Research and Reviews in Applied Sciences, Vol.1, No.3, 2009
@IJRTER-2016, All Rights Reserved 199You can also read
