Clip Art Retrieval Combining Raster and Vector Methods
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Clip Art Retrieval Combining Raster and Vector
Methods
Pedro Martins∗ , Rui Jesus∗ , Manuel J. Fonseca† , Nuno Correia∗
∗ CITI - Dept of Computer Science, FCT, New University of Lisbon, 2829-516 Caparica, Portugal
Email:rjesus@deetc.isel.ipl.pt
† Department of Computer Science and Engineering, INESC-ID/IST/Technical University of Lisbon,
R. Alves Redol, 9, 1000-029 Lisboa, Portugal
Email: mjf@inesc-id.pt
Abstract—Clip art databases can be composed by raster im- each one. The majority of the proposed image retrieval systems
ages or by vector drawings. There are technologies for searching [2], [3] are based on methods that use only one format and
and retrieving clip arts for both image formats but research has they are able to retrieve relevant clip art images. However,
been done separately, focusing on either format, without taking these systems do not exploit the benefits of another format, in
benefits of both research fields as a whole. This paper describes a terms of features available and its easy extraction. They also
study where the benefits of combining information extracted from
vector and raster images to retrieve clip arts are evaluated and
do not propose methods that combine features obtained from
discussed. Color and texture features are extracted from raster raster images with information extracted from vector graphics.
images and geometry and topology features are extracted from This paper describes and discusses the main issues of a
vector images. The paper presents several comparisons between study conducted to evaluate the combination of information
different combinations with several descriptors. The results of
extracted from both formats to retrieve clip art images. We
the study show the effectiveness of the solutions that combines
both types of features. exploit the benefits of extracting geometric and topological
information from vector images with the color and texture
information obtained from raster images. To evaluate the
I. I NTRODUCTION
generalization capability of this strategy to other types of
Clip Arts are collections of synthetic digital drawings that images (different from clip arts) that usually have only a raster
can be imported and used in a wide range of documents representation, we also described and evaluate a vectorization
and applications. Generally, these collections are organized in method of raster images.
several categories that identify the elements in the drawings
The remainder of this paper is organized as followed.
using metadata. Most of the time, this information needs to
First, the related work is described. Section III summarizes
be manually introduced, which is a very time consuming pro-
the main features of the study. Section IV presents the feature
cedure. Moreover, the user needs to know which information
extraction techniques and the methods used to segment and to
was used to categorize the collection. Some of these collections
vectorize raster images. The next section describes the clip art
can have hundreds or thousands of images and for this reason,
retrieval system used in our experiments. Then, in section VI
there is interest in developing techniques that can speed up the
is described and discussed the results obtained by our study.
process of searching and categorizing of these drawings.
Finally, the conclusions and future work are presented.
During the lasts years, many CBIR (Content-based Image
Retrieval) approaches [1] have been proposed to address this II. R ELATED W ORK
problem applied to images in general. However, clip arts are
In our study we analysed the behavior of several image
images with specific characteristics which should be included
features combined with information obtained from vector
in the design of new CBIR systems to retrieve images from a
drawings. This section presents the main techniques that had
clip art collection.
been used to describe raster images in content-based image
Currently, clip art databases are composed by images with retrieval. It also describes the main approaches that have been
more elaborated drawings (http://openclipart.org/) compared proposed to retrieve vector drawings.
with the initial collections used for instance, in Microsoft
Word. Clip art drawings are not anymore simple shapes with A. Raster Characteristics
minimal color and almost without texture. This means the color
In the last decades, image retrieval systems have used color,
and texture methods used in CBIR system can also be applied
texture, shape, topology and point of interest [1]. Some of
in clip arts since there are tools (http://xmlgraphics.apache.org/
these descriptors are described in the MPEG-7 standards [4].
batik/) to convert vector images in raster images.
Color is one of the most recognizable visual features, and there
It is common to find clip art images in two formats, raster are examples where using only color is possible to separate
images and vector graphics. Each format has specific properties different classes of objects [5]. Examples of color descriptors
that can be explored in order to extract the suitable features of are: Color Histograms [5] and Color Moments[6]. One of the
oldest and most used is the color histogram, which in some extraction and (3) search engine. The first block contains the
cases can be a very robust and effective image descriptor [5]. clip art image database. Due to the nature of the clip arts and
Strickler and Orengo [6] describe other techniques to improve the features extracted, the database includes two representa-
the performance of color histograms, such as the Cumulative tions of each clip art: raster version and vector images of the
Color Histograms and Color Moments. clip arts. The second component provides methods to extract
features. It is described in section IV. The main function of the
Texture is another attribute used to characterize raster search engine (third component) is to return to the user a set
images. Descriptors obtained from the Gabor filter bank, Edge of relevant images according to the query. At this stage, it is
Histograms and descriptors based on points of interest [7] have based on a query-by-example strategy. Our testing system also
been used in databases where images have several distinctive includes a vectorization method of raster images (explained
textures [8]. Descriptors obtained using the bank of Gabor below). In our study, we also test the performance of the sytem
filters have been widely used in image processing, including using the created image vector from the raster image, in order
classification and segmentation, image recognition and image to compare it with the results obtained with the original vector
retrieval [1], [9]. drawing. Several image ranking algorithms are implemented
Multiple shape descriptors are proposed in the MPEG- for each type of feature descriptor and for different descriptor
7 [4], [10], such as a MultiLayer Eigen Vector Descriptor combinations (see section V). We use this system to evaluate
(MLEV) and a descriptor based on the Angular Radial Trans- the results in combining features from different representations
form (ART). The ART descriptor can describe objects made up of the clip art.
by one or more regions and contains information about pixel
distribution within a 2D region. This descriptor is also invariant
Raster Query
to rotation and resistant to segmentation. An evaluation of the image
database Retrieved
MPEG-7 standards [10] mentions this descriptor is best suited Clip art
Images
Feature retrieval
for shapes made up of multiple separate regions, like clip arts, extraction system
Vector
logos and emblems. graphic
image
database
B. Vector Characteristics Descriptor
database
Vector drawings, due to their structure, require different
approaches from raster images. While the latter resort to color
and texture as main features to describe their content, vector Fig. 1. System components overview.
drawings are mainly described using the shape of their visual
elements and the spatial arrangement and relationships among
those objects. A. Vectorization of Raster Images
Park and Um [11] presented a system that supports retrieval
To avoid the need to use two replicas of the same image
of complex 2D drawings based on a dominant shape, secondary
(raster and vector) and to generalize our approach to other
shapes and their spatial relationship (inclusion and adjacency).
images (different from clip arts), a vectorization algorithm
The shapes are stored in a complex graph structure and
was developed. This generates an implicit simplification of
classified using a set of basic shapes.
the image itself. The original vector images are represented
Fonseca et al. presented an approach for the description of by different lines, curves and polygons, when these images
complex vector drawings and sketches, which uses two spatial were converted to raster images all these elements are merged
relationships, inclusion and adjacency, to describe the spatial into a single plane, causing a loss of image complexity by
arrangement and a set of geometric features to describe the removing the information of overlapped elements.
geometry of each visual element in the drawing [12]. Liang
et al. [13] developed a solution for drawing retrieval based First, we segment the raster image into color regions. Then,
on Fonseca et al. approach [12], but using eight topological we apply a processing mask to separate each region using the
relationships and relevance feedback. pixel label matrix produced by the segmentation algorithm. We
use a contour finding algorithm based on [15] to get the region
Pu and Ramani, developed an approach that analyzes edges. To create the vector image, we iterate through each
drawings as a whole [14]. Authors proposed two methods to region edges to draw each region. After that, we fill it with the
describe drawings. One uses the 2.5D spherical harmonics and mean color calculated by mean shift clustering [16]. The vector
the other uses a 2D shape histogram. images are saved in the same format as the original images and
can be used instead to extract the topogeo descriptor.
Recently, Sousa e Fonseca improved their previous ap-
proach by including distance in the topology graph used to
describe the spatial arrangement of the visual elements of the IV. F EATURE E XTRACTION
drawing [2].
In this section we describe the methods used to obtain
III. C LIP A RT R ETRIEVAL S YSTEM image descriptor vectors. The descriptor vectors will be used
when comparing images in the retrieval system. The section
To provide our study, we build a clip art image retrieval sys- starts presenting and discussing some pre-processing methods
tem that is used in our experiments. The system is composed that were applied and describes the information extracted in
by three components (see Fig. 1): (1) database, (2) feature raster and in vector images.A. Pre-processing D. Topology and Geometry Features
Before any information is extracted from the images we To describe the clip arts in the vectorial format we decided
apply several transformations to enhance our algorithms per- to use Sousa and Fonseca solution [2]. Their approach uses
formance. Some clip arts have large backgrounds and the a topology graph to describe the spatial arrangement of the
clip art drawing is not centered. To remove the irrelevant drawing, where visual elements are represented as nodes and
background we build a bounding box around the clip art topological relationships of inclusion and adjacency are coded
drawing and a new image is created with all data inside it. through the specification of links between the nodes. While
Thus, only the clip art drawing remains and the irrelevant these relationships are weakly discriminating, they do not
background is removed. We decided to use 400 pixels (keeping change with rotation and translation.
image size ratio) as maximum height and width in our system, To avoid the comparison of graphs during the retrieval pro-
as it offered a good trade off between image quality and cess, authors use the graph spectrum [22] to convert graphs into
performance. To smooth the image and remove any remaining multidimensional vectors (descriptors). To obtain the spectrum
artifacts we apply a median blur filter after. The system used, of the topology graph, authors compute the eigenvalues from
interprets transparent backgrounds as black. Some images its adjacency matrix.
include transparent backgrounds and large number of clip arts
include monochromatic images, as in Fig. 3. To solve this, we Combined with the topological information, authors also
detect the transparent background and fill it with a single color. extract the geometry of each visual element in the drawing
After testing some colors we decided to use the background to describe its content. In summary, we describe the vectorial
color that is visualized in figures 2b and 2c. representation of the clip art using the geometry of its entities
and their topological organization (topogeo descriptor).
V. I MAGE D ISTANCE M EASURING
B. Color Features
To retrieve images similar to the query, the descriptors
Based on previous work [17], two color descriptors are extracted from query images are compared with the descriptors
selected: color moments [6] and color regions [17]. This color stored in our database. As in our database, queries are made
moments descriptor extracts information of the image color using a raster or vector drawing representation of the clip art,
distribution in a 4 × 4 grid. For the color regions features, depending on the descriptors used to compare the images. To
we segment the image by color, using a segmentation system reduce statistical errors caused by the range of values of the
proposed in [18]. Then, it is created a descriptor for each descriptor vectors, each descriptor is normalized [17].
detected region. Finally, it is assigned a Bag-of- Features (BoF)
To compare the image query with the clip arts in the
descriptor [17] based on the regions present in the image.
database, a distance function between the descriptors vectors is
To compare the effects on performance, we downsampled computed. The process of comparing the query image with the
the color set on the color-based feature descriptors using a clip art images in the database is similar for each descriptor.
modified version of the downsampling algorithm described in After the distance to each vector is calculated it is divided by
[19]. The color set consists of twelve colors different from the the maximum distance found. We normalize the distances in
chosen background color. The choice of the colors is based this way, as part of the process that is used to combine image
in studies [20], [21] that suggest there are approximately 12 descriptors in queries. Thus, the distance used for the color
color which almost every culture can distinguish as different moments and Gabor filter bank is,
or ’Just-not the same’(JNS).
dist(IQ ; I)
d(IQ , I) = (1)
maxDistance
C. Texture Features and Point of Interest
where IQ and I represent the feature vector of the query
We decided to use texture and point of interest features and of a clip art image of the database respectively. The
besides color to compare the different descriptors and their function dist is the Manhattan distance when using color
performance on clip art images. Based on previous works [17], moments descriptor and the Euclidean distance for the Gabor
we select the texture features based on the Gabor filter bank filter bank. Concerning the features that are organized in BoFs,
and David Lowe’s Scale Invariant Feature Transform (SIFT) the cosine distance is applied.
[7]. We also use a bag-of-features strategy when applying the To calculate the distance between images using a combi-
SIFT descriptor [17]. nation of descriptors, we calculate their distance using each
descriptor separately (equation 1) and compute the average
value of each distance. Then, we divide the average value
by the maximum distance found to normalize it. Equation 2,
summarizes the distance function used when calculating the
distance between the query image and images in the database
using multiple descriptors.
(a) (b) (c)
n
1
P
Fig. 2. Color Reducing: 2a) original image; 2b) full color set; 2c) n di (IQ , I)
JNS color set. i=0
d(IQ , I) = (2)
maxdmetric. This measure combines the concepts of precision,
relevance ranking and recall. It is computed using equation
3,
(a) Bulbs (b) Car (c) Fish (d) Flower n
P
[P @(r) × rel(Ir )]
r=1
AvgP = . (3)
total relevant images
(e) Hammer (f) Magn. Glass (g) Phone (h) Sword where n is the number of retrieved results, P @(r) symbol-
izes the ’precision at’ measure which refers to the precision of
the results until the rth position and rel(I) is a binary function
that returns 1 if the image is relevant and 0 otherwise. To
evaluate several queries we used the mean average precision
(i) Tree (j) TV (k) Misc (MAP), the average AvgP across all the queries. Table II shows
the MAP results obtained in this test by each descriptor: color
Fig. 3. Clip art categories in the database. moments (CM), Gabor filters features (Gbr), color regions
(CR) descriptor, SIFT descriptor, topology and geometry
TABLE I. C LIP ART TEST DATASETS .
features obtained from original images (TP) and from raster
Categories DataSet1 DataSet2 images (rTP).
Bulbs 10 41
Car 10 168
Fish 10 88 TABLE II. R ETRIEVAL MAP RESULTS .
Flower 10 309 Category CM Gbr CR SIFT TP rTP
Hammer 10 29 Bulbs 0,34 0,33 0,23 0,24 0,11 0,19
Magn. Glass 10 21 Car 0,37 0,57 0,32 0,23 0,28 0,16
Phone 10 50 Fish 0,20 0,24 0,25 0,21 0,17 0,20
Sword 10 53 Flower 0,31 0,26 0,22 0,23 0,12 0,13
Tree 10 73 Hammer 0,18 0,12 0,17 0,20 0,26 0,16
TV 10 165 Magn. Glass 0,38 0,18 0,18 0,19 0,17 0,16
Misc - 11888 Phone 0,49 0,48 0,52 0,21 0,22 0,18
Total 100 12885 Sword 0,62 0,41 0,41 0,31 0,21 0,20
NQueries 100 20 Tree 0,23 0,57 0,28 0,18 0,21 0,20
TV 0,52 0,24 0,26 0,37 0,16 0,14
Where n is the number of used descriptors and maxd is the Avg 0,36 0,34 0,28 0,24 0,19 0,17
maximum average distance of the combined descriptors. After
computing the distances, the results are ordered by ascending These individual results set our base of comparison. The
order of distance and returned to the user. original vectorial images (TP) showed similar results in almost
all the retrieval tests, when compared to the simplified vector
VI. E XPERIMENTS images created from the raster images (rTP). In comparison
with the raster descriptors, this descriptor had weaker results
To conduct our study we provide several test with the in almost every category except in the ”Hammer” category.
descriptors described in section IV, using two different col-
lections of clip arts. One with 100 images divided into 10 Among the raster descriptors the color moments obtained
categories (dataset1) and another with approximately 13000 the best results, achieving a MAP of 36%. This result was
images divided into 11 categories (dataset2). All the images obtained using the JNS color set. We decided to use it instead
were obtained from the OpenClipArt library1 . To find the better of the full color set based on a set of previous experiments. In
combination of descriptors, first, we tested each descriptor these experiments, the JNS color set increased the performance
separately and then with others. We evaluated every algorithm in approximately 2% in MAP.
globally and by category, the evaluation metrics and tests are
described in the next sections. The highest results we achieved were in the ”Sword”,
”Phone” and ”Tree” categories. This was due to two properties:
(1) most images in these categories have similar colors (a
A. Controlled dataset large number are monochromatic) and (2) the images had
Table I and Fig. 3 show the results of the manual label- lesser width than others allowing the descriptor to extract more
ing and examples of images in the data sets. The first test relevant information.
performed uses the dataset1 where each image was previously The BoF descriptor of color regions (CR) did not get such
classified into 10 different categories: Bulbs, Car, Fish, Flower, good results when compared with the CM descriptor. The
Hammer, Magnifying Glass, Phone, Sword, Tree and TV (see process of finding the correct number of codewords that make
figure 5). In this experiment, each image of this data set is up the codebook was difficult. Despite that, we still achieved
used as query-by example. our best results in the ”Fish” and ”Phone” categories, approx-
In order to include the position of the relevant clip arts in imately 25% and 52% MAP respectively, with this descriptor,
the results, we measured the results using the average precision using a codebook with 500 codewords. This measured a 4%
increase in MAP in both categories compared to the color
1 http://openclipart.org/ moments descriptor.We kept the default parameters in the texture descriptor
extraction algorithm using the Gabor filters [9]. We obtained
a MAP of 34%, very close to color moments descriptor,
the second best overall results in queries using only one
descriptor. It obtained the highest results in the ”Car” and
”Tree” categories with a MAP of approximately 57% in both
categories.
Finally, the SIFT BoF descriptors showed weaker results
than the texture filters extracted with the Gabor filters.
B. Large size dataset
To evaluate the performance of the algorithms with a larger
number of images, we built a more realistic dataset using a
collection with around 13000 images (dataset2). From each Fig. 4. Retrieval of 5 relevant images.
category of the dataset1, 2 images are removed to be used as
image query and the remaining ones are added to the dataset2 • Color moments and texture extracted with the Gabor
collection. These images were divided manually into the same filters.
categories as in the first test. However, the new collection is • Color moments and BoF from SIFT descriptors.
composed by a large number of random clip arts. Therefore,
we added a ”Misc” (Miscellaneous) category, to include all • Color moments and topogeo descriptor.
the images that do not belong to any of the former categories.
Table I shows the number of images that each category has These combinations categorize images using color, texture,
after each image in the collection was labeled. shape and topology. Then, we ran the two tests described in the
For the second test we used the R-precision metric to previous sections. In the first test, the first two combinations
evaluate our results. We chose 5 for the value R. To evaluate showed improved results, while adding the TP descriptors
across multiple queries we used the average R-precision of reduced the performance. Table IV shows the results obtained
each query. The results are shown in table III and figure 4. with larger database. The combination with the SIFT descrip-
tors decreased the performance while the combination with TP
TABLE III. AVERAGE R-P RECISION RESULTS FOR RETRIEVAL OF 5 improved the result obtained using only the CM descriptors.
RELEVANT IMAGES .
We tested three more combination, where we com-
Category CM Gbr SIFT TP rTP
Bulbs 0,027 0,003 0,002 0,005 0,004 bined three descriptors: CM/SIFT/TP, CM/Gbr/TP and
Car 0,061 0,337 0,049 0,009 0,031 CM/Gbr/rTP. In the first test, with the controlled database, the
Fish 0,007 0,012 0,008 0,015 0,008 CM/Gbr/TP combination had the second best result overall.
Flower 0,361 0,084 0,262 0,049 0,054
Hammer 0,007 0,003 0,006 0,003 0,014
When compared to the Color Moments/Gabor combination,
Magn. Glass 0,174 0,008 0,005 0,004 0,004 which was the best in the first test, this descriptor had almost
Phone 0,276 0,020 0,031 0,013 0,019 the same MAP, with 0,3 % less using the CM/Gbr/rTP
Sword 0,199 0,097 0,037 0,008 0,004
Tree 0,020 0,064 0,004 0,011 0,006
combination.
TV 0,193 0,073 0,066 0,015 0,015
Avg 0,13 0,07 0,05 0,02 0,01 In the second test (see table IV), the combination
CM/Gbr/TP scored our best results overall, with the highest
The results we obtained on this test were weaker but the average R-precision, as you can see in Fig. 4. We also tested
performance of the descriptors was analogous with the results this algorithm with the simplified vector images. This version
obtained in the previous test. The color moments descriptor of the algorithm had almost the same average R-precision of
achieved the best results overall once again. Also the second the combination that use the original vector drawings. It also
best result continued to be the Gabor filter bank descriptor. achieved better results in 5 categories, as we can se in table
The greatest decrease in performance was seen in the BoF IV. This combination has the advantage of to avoid the need of
descriptor made from SIFT descriptors. Compared to the other two copies of the query image, because it generates the vector
raster descriptors the SIFT descriptor obtained the weakest images from the raster versions.
results in most categories besides the ”Flower” category.
Our results showed that the combination of certain descrip-
The simplified vector images showed improved results. tors representing distinct characteristics improved the retrieval
Comparing to the descriptors extracted from the original vector results. The higher degree of information makes the approach
images, the difference between the average values obtained to be more robust in a database with a larger number of
in the second test is minimal, but we could see significant images. We could have used a shape descriptor to extract
improvements in the ”TV” and ”Sword” categories. information from raster images, like the ART but it does not
extract topology information. Besides, the results showed by
C. Descriptor Combination the topology and geometry extracted from the original vector
images and those created by our vectorization of raster images,
To compensate the disadvantages found in some algorithms had similar results. This means that it is possible to retrieve
we combined the following pairs of descriptors: clip art images using only the raster version.TABLE IV. AVERAGE R-P RECISION RESULTS WITH COMBINATION OF DESCRIPTORS FOR RETRIEVAL OF 5 RELEVANT IMAGES .
Category CM/Gbr CM/SIFT CM/TP CM/SIFT/TP CM/Gbr/TP CM/Gbr/rTP
Bulbs 0,008 0,009 0,039 0,015 0,012 0,009
Car 0,387 0,066 0,084 0,088 0,364 0,366
Fish 0,009 0,008 0,006 0,008 0,010 0,010
Flower 0,482 0,340 0,443 0,384 0,548 0,477
Hammer 0,005 0,004 0,008 0,007 0,005 0,006
Magn. Glass 0,265 0,042 0,279 0,047 0,323 0,335
Phone 0,127 0,099 0,290 0,106 0,209 0,275
Sword 0,356 0,099 0,264 0,135 0,342 0,414
Tree 0,048 0,015 0,024 0,017 0,052 0,040
TV 0,212 0,184 0,100 0,163 0,256 0,153
Avg 0,19 0,09 0,15 0,10 0,21 0,21
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