Relevance Tuning in Content-Based Retrieval ofStructurally-Modeled Images using Particle Swarm Optimization

Nozomi Oka, Nonmember and Keisuke Kameyama, Member, IEEE

Abstract—Similarity of images in content-based image re-trieval (CBIR) is a subjective measure varying by the user,and requires tuning according to the user’s preference. Anotherissue in CBIR is the need of partial image matching. Structuralmodeling of the images can be promising in finding a smallquery image within a large database image. In this work, agraph-based image modeling which assigns image regions tolabeled nodes and their adjacency to weighted edges is used.Also, the image similarity measure is tuned according to theuser’s evaluation, by way of parameter selection using ParticleSwarm Optimization (PSO)[1][2]. In the experiments, a small-scale CBIR system based on graph modeling of images wasdeveloped. Using the system, it was confirmed that imagesincluding the query image of different size and rotation anglecould be successfully retrieved. Also, the user’s preference inweighting the different aspects of similarity in the feedbackinformation was found to be successfully incorporated in theretrieval after parameter optimization using PSO.

I. INTRODUCTION

Due to the proliferation of digital images, the importanceof the search function in an image database is ever increasing.Today, majority of image search engines rely on metadatasuch as keywords. However, the labor required for keywordassigning can be costly, and the results tend to reflect theassignee’s subjective views. In order to solve this problem,research on content-based image retrieval (CBIR) have beenconducted by many groups and researchers. CBIR aims touse image features for indexing, thereby dispensing keywordassignment work and implementing a more flexible searchstyle.

Nevertheless, similarity of images is in itself a verysubjective measure, which can differ user by user, or caseby case. To satisfy various search preferences of the users,a function called relevance feedback becomes important ina CBIR system. It seeks the user’s evaluation against theCBIR’s retrieval results, and tries to reflect the preferenceinformation to the searches that follow.

Another issue in CBIR is the implementation of partialimage matching. A structural modeling of the images will berequired to find a small query image within a large databaseimage. One method of structural modeling that enables sucha matching is image modeling using graphs.

In this work, a graph-based image modeling is employed,which assigns image regions to labeled nodes and theirneighboring relations to weighted edges. When this schemeis used, partial image matching becomes a search of anisomorphic subgraph with sufficiently similar feature vectors

N. Oka and K. Kameyama are with the Graduate School of Systemsand Information Engineering, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8573,Japan (email: Keisuke.Kameyama@cs.tsukuba.ac.jp).

ModelingQuery

Image

Database

Image

Matching Ranking

Modeling Major

Division

Sub Class

Sub Class

Sub Class

Sub Class

.

..

Sub

Class

Selection

Selected

Sub Class

Relevance Feedback

Fig. 1. A general scheme of content-based image retrieval (CBIR) withsimilarity tuning by the user’s relevance feedback.

assigned to their nodes and edges. Also the image similaritymeasure is tuned according to the user’s evaluation, by wayof parameter selection using Particle Swarm Optimization(PSO) [1]. In the experiments, a laboratory-scale CBIRsystem based on graph modeling of images was developed.Using the system, it was confirmed that images includingthe query image of different size and rotation angle could besuccessfully retrieved. Also, the user’s preference in the feed-back information was found to be successfully incorporatedin the retrieval after parameter optimization using PSO.

In the following, the current state of the art of CBIRwill be reviewed in Sec. 2. In Sec. 3, the proposed CBIRscheme with graph-based image modeling and parameteroptimization according to the user’s relevance feedback willbe introduced. Results of the experiments will be shown anddiscussed in Sec. 4, and the paper will be concluded in Sec.5.

II. CONTENT-BASED IMAGE RETRIEVAL (CBIR)

A. State of the art

Content-based image retrieval (CBIR) is a technique forretrieval from an image database. In contrast to retrievalbased on keywords, CBIR uses the query image submittedto the system. In Fig. 1, the general procedure of CBIR isshown. The images in the database and the query image gothrough a common modeling process. The database imageswill be optionally divided to subclasses for retrieval effi-ciency to narrow the candidates that match the query image.The distance evaluation between the query and each of thedatabase images will be made using the modeled version ofthe images. Using the estimated distances, a subset of thedatabase images will be ranked by their similarity to thequery. This ordered set of images will be the retrieval.

Among the systems developed in the CBIR research are,TRADEMARK, ART MUSEUM [3], QBIC [4], Photobook[5], Viper [6] and many others.

978-1-4244-2771-0/09/$25.00 ©2009 IEEE

B. Modeling for partial matching

The modeling stage will convert the images to theirabstract descriptions. In order for the matching stage thatfollow to be effective, it is important that the model retainsthe necessary image feature. In the literature, modeling basedon extraction of local features where locality is defined bytiled subimage region [3] [7], and piecewise approximationof image contours [8][9] have been reported. The modelingbased on image coordinate (such as the former examples thatuse grid segmentation) suffer from translation and rotationvariance of the model. In order that the query image canbe matched against a portion of the database image, anaffine-transformation invariant modeling will be necessary.One promising method of modeling will be the graph-basedmethod, which maps the regions and their adjacency to nodesand edges.

C. Relevance feedback

Although image similarity can be defined as a functionof low-level feature of two images, the preferred definitionwill be different for each user. In order to meet the user’svarying preferences of image similarity, the user’s evaluationto the retrieval, or the relevance feedback, can be utilized totune the modeling and distance evaluation as shown in Fig.1. This tuning process can be formalized to an optimizationproblem of the similarity function parameters.

In a recent paper, Pedrycz et al.[10] introduced an imageclustering method which is based on angular spectral imagefeature and unsupervised fuzzy c-means clustering. There, byusing a clustering algorithm with partial supervision[11] thatemploys an augmented objective function incorporating classinformation (corresponding to the user’s relevance feedbackto a subset of the image database), it is reported that theclustering performance is improved. The method we take forutilizing the relevance feedback in this work is to modify(optimize) the image distance evaluation function, so thatthe user’s preference will be met.

D. Aim of this work

According to the observations above, the aim of this workare to develop a scheme of CBIR with the following features.

1) Robust retrieval based on graph modeling and partialimage search by subgraph matching

2) Parameter optimization of similarity evaluation usingrelevance feedback

III. STRUCTURAL IMAGE MODELING AND RELEVANCE

OPTIMIZATION

A. Graph-based image modeling

The structure of the image will be modeled by an attributedand partially directed graph. Matching of the query image asa part of a database image will be done as shown in Fig. 2

The graph-based image modeling is done in the followingsteps.

Graph modeling

Matching

Query image

Database image

Fig. 2. Graph-based image modeling and matching of the query imageinside a database image as a subgraph matching

Input imageSegmentation 2

(lower layer : 4 regions)

Segmentation 1(upper layer : 3 regions)

y

x

y

x

y

x

765

41

32

7

6

5

4

3

2

1

x

y

Generated graph

Fig. 3. An example of region segmentation and hierarchical graphmodeling. Numbered nodes correspond to segmented regions. Edges in grayand black signify inter-layer inclusion by the upper node and intra-layerregion contiguity, respectively.

1) Hierarchical segmentation and the image graph: First,the image will be segmented to local regions.

In this work, segmentation was done by the followingprocedure which is a simplified variant of JSEG [12].

• Extract region borders using the Sobel filter and labelthem as border pixels.

• Label contiguous non-border pixels as regions.• Concatenate neighboring regions to the desired region

granuality.

The segmentation will be done in multiple granualityhierarchies. The graph modeling the whole image is denotedas G(N, E), where N = n1, . . . , nP and E = (ni, nj)are sets of nodes and edges in the graph. Graph G has anhierarchical structure, where nodes in the upper level will beassigned a region of coarse segmentation, and those in thelower levels will be assigned smaller regions within a largerregion in the upper level.

2) Edge assignment: In Fig. 3, an example of regionsegmentation and edge assignment is shown. Regions of eachsegmentation layer are connected by edges for the nodes ofthe regions that are contiguous. Also, directional inter-layeredges are provided from an upper layer region node to alower layer node whose region is included.

B. Node features and distance

1) Node features: Features of image region i are denotedas a node feature vector ri = [cri, cgi, cbi, δi, βi]. The featurecomponents of region i are listed in the following.

• Average color

ci = (cri, cgi, cbi) (0 < cri, cgi, cbi < T )

having RGB components. Here, T denotes the maxi-mum component level.

• Roundness

δi = S′i/Si (0 < δ < 1)

where Si is the area of region i. Here, we assume acircle having area Si centered at the region i’s center ofgravity. The overlapping area of this circle and region iis S′

i.• Inverse shape complexity

βi = Li/Si (0 < β < 1)

where Li is the number of pixels in the outer border ofregion i.

2) Node distance: The distance between nodes ni and nj

is defined as,

Dn(ni, nj) = W1||ci−cj ||2E+W2|δi−δj |+W3|βi−βj | (1)

where W1, W2 and W3 are given positive weights.

C. Edge features and distance

1) Edge features: Relations of two regions will be mod-eled as edge feature vector eij = [ρij , γij , αij ] for edge(ni, nj). The following are the components of eij .

• Color difference

ρij = ||ci − cj ||2E (0 < ρ < 3T 2)

• Area ratio

γij = Si/Sj (1/S0 < γij < S0)

where S0 is the area of the whole image.• Relative angle αij (0 < αij < π) is the angular

difference between the gravity centers of two regions,measured at the image center.

2) Edge distance: The distance between edges eij =(ni, nj) and ek� = (nk, n�) is given as,

De(eij , ek�) = W4|ρij−ρk�|+W5|γij−γk�|+W6|αij−αk�|(2)

with W4, W5 and W6 being given positive weights.

D. Soft subgraph matching

Matching of query image within the database image willamount to finding a subgraph in the database image graphwhich is similar to the query image graph. In the literature,there are known matching algorithms for matching a graphand a subgraph of another graph such as the VF algorithm[13]. For the partial image matching in this work, use ofconventional subgraph matching algorithm is not appropriate,because, (1) strictly matching subgraphs are almost neverfound in database image graphs and a soft matching schemeallowing certain level of difference has to be employed, and(2) a near- exhaustive search is required for finding a best-matching subgraph even after a similar subgraph is found.

In order to meet these requirements, the VF matchingalgorithm was altered in the following way.

• Error margins in matching nodes (En(ni, nj) < θn) andedges (Ee(eij , ek�) < θe) are provided to allow a softmatching.

• Search continues exhaustively for a best-matching sub-graph even after a subgraph of admissible difference isfound.

E. Image distance

By using the modified subgraph matching algorithm, theimage distance will be evaluated against the best-matchingsubgraph in the database image graph. The best-matchingsubgraph is chosen to have the maximum number of nodessatisfying En < θn (matched nodes), and minimum averagenode distance Dn.

The final distance between the two graphs (images) isdefined as

D = Ds + Df (3)

where

Ds = 1 − Nc

Nq(4)

Df =

∑p∈P Dn(p)

Nc. (5)

Here, Nc, Nq and P denote the numbers of matched nodes,number of nodes in the query graph, and the set of matchednode pairs, respectively. Therefore, Ds denote the node-mismatch rate, and Df is the average node distance mea-sured by the node feature vectors. The database images willtypically be ranked according to their similarity (smallnessof the distance) to the query image, and will be presented tothe user as the retrieval result.

F. Image distance parameters

As pointed out in Sec. 2, definition of the required imagesimilarity differs case by case. In order to meet the require-ments and the user’s preferences, the image distance functionwill be tuned by way of parameter selection making use ofthe user’s relevance feedback.

The image distance D in Eq. (3) is a function of queryand database images. It is also a function of predefinedparameters used in node matching (W1, W2, W3 and θn), and

those in edge matching ((W4, W5, W6 and θe). By changingthe parameter values, the significances of the differences ineach node and edge feature can be varied. For example, amatching sensitive to color difference may prefer a largevalue for W1. When shape differences are not very important,small W2 and W3 may be appropriate.

In order to meet various query requirements and userpreferences, the parameter sets should be chosen accordingly.The best parameter set for a particular type of preferencemay be sought as a solution to an optimization problem inwhich the preference is expressed as a scalar score function.In doing this, a robust and efficient optimization scheme isin need.

G. Particle Swarm Optimization (PSO)

Particle Swarm Optimization is an optimization methodwhich employs a parallel search of multiple points, eachupdated along a continuous trajectory within the searchspace. The method was proposed by Kennedy and Eberhartin 1995[1][2].

An agent called the particle moves according to a simplelaw incorporating its individual observation, memory andsome information shared among the group (swarm). The i-th particle holds its current coordinate in the search spacexi(t) and its moving velocity vi(t). The fitness of xi(t) isevaluated by an oracle and fed back to the particle. Eachparticle has a memory of its maximally evaluated coordinateso far (pi). Also the best coordinate in the swarm so far gis shared by all the particles.

Each particle moves through the parameter space accord-ing to the updating rules of coordinate and velocity as,

xi(t + 1) = xi(t) + vi(t + 1), (6)

vi(t + 1) = vi(t) + λ1(pi − xi(t)) + λ2(g − xi(t)).(7)

As seen in Eq. 7, the velocity is updated towards thehighly evaluated points of the particle and the swarm. Themagnitudes of these influences are controlled by parametersλ1 and λ2.

Although using this simple rule, the swarm can find theoptimum in various benchmark optimization problems veryefficiently.

H. Relevance optimization scheme

The parameter optimization flow using the user’s feedback(assessment) to the retrieval is shown in Fig. 4. The user’spreference is represented by an assessment function J whichevaluates the retrieval R being a function of the parametervector

x = [θn, θe, W1, W2, W3, W4, W5, W6]T . (8)

In each epoch, the parameter set of all particles will beevaluated by function J thus enabling the optimization. Thisprocedure can be described in a pseudo-code as follows.

Training query I

Databaseimage set B

Distance evaluation algorithm

Parameter space

Retrieval (ranking)

Parameter set

i

i

R(I, B; x )

AssessmentJ(x )

iJ(x )

J(x)

x

i

i

x

Fig. 4. Scheme of parameter optimization for the image distance functionD and the retrieval rankings using PSO.

1 Begin2 set G ← Maximum epoch3 set H ← Swarm Size4 for i = 1 to H5 Initialize xi and vi

6 for t = 1 to G {7 for i = 1 to H {8 Set xi(t) to the image distance

evaluation algorithm (D) and obtainimage retrieval R(xi(t)) usingquery and dictionary image sets.

9 Evaluate retrieval as J(R(xi(t))).10 Calculate xi(t + 1) and vi(t + 1)

using Eqs. (7) and (7).11 Update pi as necessary.12 }13 Update g as necessary.13 }14 return g as the optimized parameter set.15 End

IV. EXPERIMENTS

The effectiveness of the proposed framework of CBIR withgraph modeling of images, and the optimization of imagedistance function parameters by use of relevance feedbackwill be evaluated. The experiments will be focused on theproposed method’s ability to retrieve scaled and rotatedimages, and change in the ranking of retrieved images byparameter optimization according to different preferences.

A. Conditions

1) Image attributes and function J: In the database im-ages, image groups having common attributes such as shapeand color combination were pre-defined. The ranking of theretrieved images were evaluated by function J which countsthe number of target group images in the highly ranked

retrievals. The general form of the evaluation function is

J =N ′

N. (9)

Here, N is the size of the target image group, and N ′ is thenumber of target group images that were ranked in top Nrelevant images. The best case is when all the N images arefrom the target group (J = 1), and the worst case is whenthe top N includes no target group images (J = 0).

2) Optimization conditions: For a query image, a targetimage group were selected, which reflects the virtual user’spreference. The 8 parameters in Eq. (8) were tuned tomaximize J using PSO. Particle numbers and maximumoptimization epochs were set to 20 and 50, respectively.

B. Experiment 1: Tile images

In this experiment, a set of tile images which allow simplemodeling were used.

1) Tile image set: The images used in this experimentis a collection of computer-generated images consisting ofsquare color tiles, connected in various patterns. The setshown in Fig. 5 consists of images of 7 different tiled patternsand 8 different color combinations each attribute denoted by”pattern type” and ”color type”, respectively. The whole setincludes the 56 images in 2 different scales; images in oneof them being exactly the half size of those in the other withan additional flip along the horizontal axis. The dimensionsof the large and small images are 256 × 256 and 128 × 128pixels, respectively.

2) Procedure: The query images were selected from thetile image set. Groups of images having same patterns andcolor combinations were formed, each consisting of N =16 and N = 14 images, respectively. In Fig. 5, the same-pattern groups correspond to paired columns and the same-color groups correspond to rows. The number of layers inthe image graph was set to 1, since the images include onlysimple color region structures.

For a query image, the parameters were optimized for twodifferent target groups, namely the same-pattern group andthe same-color group to which the query image belongs. Eachof them corresponds to the case when the virtual user pre-ferred to retrieve images having similar color combination,and similar shapes, respectively.

After the optimizations, a novel query image was used toevaluate the retrieval rankings for both optimized parametersets.

3) Results and discussion: When similar color combina-tions were preferred, the parameter sets were changed fromthe default setting as shown in Table I. The evaluation Jimproved from 0.79 to 0.93 by parameter tuning. The resultof retrieval for an image not used for training is shown inFig. 6.

In Fig. 6, 9 images among the top 10 are having thesame color combination as the query. Therefore, the user’spreference for images having similar color combination hasbeen met.

TABLE I

PARAMETER SET BEFORE AND AFTER OPTIMIZATION WHEN COLOR

SIMILARITY WAS PREFERRED IN THE TILE IMAGE SET

0.01 0.0170.1901.0000.2640.1270.9970.065

W W W W W Wθθ 1n e 2 6543

Before

After

0.05 0.8 0.40.1 0.4 0.10.10.1

Optimization

Parameter

1 2 3 4 5

109876

rank

rank

image

image

(a)

(b)

Fig. 6. Retrieval after optimization when color similarity was preferred.(a) Query image. (b) Ranked retrieval result.

In Table I, it is seen that W1 has increased after optimiza-tion. This means that more weight has been put on colorfeature. Also, decrease of θn signifies a stricter error marginfor the node matchings, where node (region) features aremostly the same except for color in this tile image set.

Parameter changes and the retrieval result for when simi-larly patterned images were preferred, are shown in Table IIand Fig. 7. The evaluation J improved from 0.25 to 1.00 byparameter tuning. Here, all the top 10 similar images havethe same pattern as the query, thus perfectly meeting thepreference.

For the parameter values, W1 is decreased and θn is nowset to a relatively large value. This reflects the lower priorityto color similarity and a loose policy for region features. Onthe other hand, similarity in relative placement of regionsis important to retrieve images having same patterns. Thisexplains the increase of W6, where relative angle of theregions exactly reflect such attribute of patterns.

These two results show that the virtual user’s preferencesare appropriately reflected by selecting the parameter valuesin evaluating the image distance D.

C. Experiment 2: COIL images

In this experiment, an image set of real-world objects areused. However, the objects are observed in a very controlledcondition such as black backgrounds. Here, matching of real-world objects modeled in a hierarchical graph will be tried.

1) COIL image set: The second image set is shown inFig. 8. This set originates from the image set for objectrecognition COIL-100 [14]. Each database image is a collageof four COIL-100 images scaled down to a half size. Partof the images have their colors intentionally altered from the

patterntype

colortype

a

b

c

d

e

f

g

h

1 1 2 2 3 3 4 4 5 5 6 6 7 7

Fig. 5. ”Tile” image set

1 5432 76

1098 141211 13

151716 2018 19 21

252422 23 26 27 28

29 3534333230 31

Fig. 8. ”COIL” image set

TABLE II

PARAMETER SET BEFORE AND AFTER OPTIMIZATION, WHEN PATTERN

SIMILARITY WAS PREFERRED IN THE TILE IMAGE SET

0.499 0.8390.7040.4700.5110.2040.6610.534

0.05 0.8 0.40.1 0.4 0.10.10.1

W W W W W Wθθ 1n e 2 6543

Before

After

Optimization

Parameter

1 2 3 4 5

109876

rank

rank

image

image

(a)

(b)

Fig. 7. Retrieval after optimization when pattern similarity was preferred.(a) Query image. (b) Ranked retrieval result.

original. Each image has a dimension of 128 × 128 pixels.The query image which includes a single object was chosenfrom the COIL-100 images without collaging or scaling.

2) Procedure: Images no. 1 through 6 in the COIL imageset all include the same object with slightly altered color tints.These images will form the target group of size N = 6. Thiscommon image of a yellow box with red characters in itsoriginal size will be used as the query. The number of layersin the image graph was set to 3, because retrieval of queryimage embedded in a different size is aimed.

The optimization was done for the query image and theaforementioned target set. Therefore, the query object regard-less of some changes in color was sought in the database.

3) Results and discussion: The changes in the parameterset and the retrieval ranking are shown in Table III and Fig.9, respectively. The evaluation J was improved from 0.34to 0.84. The changes in the parameter set (Table III) showsame tendencies seen Table 6, where priority was given toshape similarity.

In Fig. 9, 5 among the top 6 images are from the targetgroup, after optimization. The only image not from the targetgroup ranked 4th, also includes a box which is similar to thequery. Although the images are not shown here, the sameretrieval before parameter optimization gave 3 among thetop 6. Therefore, it is confirmed that the retrieval ranks areimproved by optimization in images modeled in hierarchicalgraphs.

However, image segmentation methods requires a furtherfine tuning especially when modeling the small objects.

TABLE III

PARAMETER SET BEFORE AND AFTER OPTIMIZATION, WHEN SHAPE

SIMILARITY WAS PREFERRED IN THE COIL IMAGE SET.

0.023 0.3540.3300.3110.5840.2990.5770.292

0.05 0.8 0.40.1 0.4 0.10.10.1

W W W W W Wθθ 1n e 2 6543

Before

After

Optimization

Parameter

1 2 3 4 5

109876

rank

image

model

image 1

rank

image

model

image 1

model

image 2

model

image 3

model

image 2

model

image 3

(a) (b)(c)

Fig. 9. Retrieval of COIL images when shape similarity was preferred.(a) Query image. (b) Query image model. (c) Ranking of retrieved imagestogether with their 3-level models.

V. CONCLUSION

In this work, a content-based image retrieval (CBIR)scheme which utilizes a graph-based image modeling forpartial image matching, and image similarity parameter op-timization using Particle Swarm Optimization (PSO) wasintroduced. In the experiments, it was confirmed that imagesincluding the query image of different size and rotation anglecould be successfully retrieved. Also, the user’s preference inweighting the different aspects of similarity in the feedbackinformation was found to be successfully incorporated in theretrieval after parameter optimization using PSO.

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