Content Based Image Retrieval through Featureslike Color, Texture and Shape

B Narasingh Patro*Dept. of Computer Application, Noida Institute of Engg. & Tech. (NIET), Greater Noida, Up, India,

narasingh.patro@gmail.com

Abstract-As an Internet Network and with the ongoingdevelopment of multimedia technology is desired a process,that successful retrieval to images from large-scale imagecoUections with comprehensive result. Yet, it is a problem inthe field of multimedia digital libraries. The user are notsatisfied with the traditional information retrievaltechniques, because the use of textual description torepresent and query images may provide poor result, due tosubjectivity of descriptions. In context to find an imageefficiently and effectively from large image collections byusing special important low-level visual features are likescolor, texture and shape of an image. We know, Image has tobe represented with certain important low-level visualfeatures are likes color, texture and shape. In this paper, Ipropose an efficient and effective image retrieval technique,which is Content Based Image Retrieval (CBIR) that usesdynamic dominant color, texture and shape features of animage.

1. INTRODUCTION

Content-Based Image Retrieval (CBIR) aims atdeveloping techniques that support effective searchingand browsing to digital images based on derived imageryfeatures. In this various application domains such asentertainment, commerce, education, biomedicine, andcrime privation are uses hues amount of digital image dataso, CBIR has become a prominent research topic becauseof the proliferation of image data in digital form.Increased bandwidth availability to access the internet inthe near future will allow the users to search for andbrowse through image databases located at remote sites.

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Fig. I Retrieval Analyzer

In typical content-based image retrieval systems, thevisual contents ofthe images in the database are extractedand described by multi-dimensional feature vectors. The

feature vectors of the images in the database form afeature database. To retrieve images, users provide theretrieval system with example images or sketched figures.The system then changes these examples into its internalrepresentation of feature vectors. The similaritiesdistances between the feature vectors of the queryexample or sketch and those of the images in the databaseare then calculated and retrieval is performed with the aidof an indexing scheme. Content-based image retrievalsystem is built using statistics, pattern recognition,computer and signal processing. The indexing schemeprovides an efficient way to search for the image database.Recent retrieval systems have incorporated users'relevance feedback to modify the retrieval process inorder to generate perceptually and semantically moremeaningful retrieval results

To estimate the local features around significantcurvature points, it is necessary to select window size andshape properly. A large window may incorporate lot ofinsignificant information (noise) along with significantinformation, whereas small ones may leave out a lot ofimportant information. Hence, the estimation of localfeatures on a fixed sized window will have somelimitations, as the spread of significant information maybe different depending on the type of curvature points(sharp, medium or weak). The characteristics of sharpcurvature points will be confined within a small region,while for those of medium and weak type, the region willbe larger. These facts indicate the usefulness of extractingthe possible high curvature region of interest (ROI),whose shape and size vary adaptively according to thenature of curvature type.

II. SCOPE

The scope for CBIR system is to integrate with trustedtechniques so as to process images of diversifiedcharacteristics and categories. Many techniques forprocessing of low level cues are distinguished by thecharacteristics of domain-images. The performance ofthese techniques is challenged by various factors likeimage resolution, intra-image illumination variations,non- homogeneity of intra-region and inter-region

42 NIET Journal of Engineering & Technology, Vol. 5, 2014

textures, multiple and occluded objects etc. The othermajor difficulty, described as semantic-gap in theliterature, is a gap between inferred understanding /semantics by pixel domain processing using low levelcues and human perceptions of visual cues of givenimage. In other words, there exists a gap betweenmappings of extracted features and human perceivedsemantics. The dimensionality of the difficulty becomesadverse because of subjectivity in the visually perceivedsemantics, making image content description a subjectivephenomenon of human perception, characterized byhuman psychology, emotions, and imaginations. Theimage retrieval system comprises of multiple inter-dependent tasks performed by various phases. Inter-tuning of all these phases of the retrieval system isinevitable for over all good results. The diversity in theimages and semantic-gap generally enforce parametertuning & threshold-value specification suiting to therequirements. For development of a real time CBIRsystem, feature processing time and query response timeshould be optimized. A better performance can beachieved if feature dimensionality and space complexityof the algorithms are optimized. Specific issues,pertaining to application domains are to be addressed formeeting application-specific requirements. Choice oftechniques, parameters and threshold-values are many atimes application domain specific e.g. a set of techniquesand parameters producing good results on an imagedatabase of natural images may not produce equally goodresults for medical or microbiological images.

III. EXPERIME TS AND RESULTS

We kept research with images of four CORELdatabases which has images of different size and ofdifferent resolution. We take the results for the bothversion namely (i) color-shape and (ii) color-texture of theimproved CLUE algorithm on each database. We carriedout our conduct experiments with a general purpose imagedatabase, which contained approximately 1,000 images.Our system used the same feature extraction technique asgiven in [4] and we used the Euclidean distance as thesimilarity measure for computing the similarity betw eenthe query and target images in the database. Ourimplementation provides a Random option that gives auser a random set of images from the image database tostart with. Once a query image is received, the systemdisplays a list of computed similarity measure values forthe different images in the database. Then, it displays a listof images in decreasing order of their similarity with thequery image. Now, we present the top 25 results due tospace limitation from the proposed CBIR system and

shown one result from each resolution of image databaseby randomly chosen query images as shown in Figures 2,Figure 3, Figure 4 and Figure 5. To compare theperformance ofthe proposed CBIR system with the othertwo CBIR systems based on CLUE and UFM, we test onevery images and take each image as a query image fromthe 1000 image database. The database, we used, is asubset of the COREL database and it contains 100 imageseach from the 10 image categories and hence, a total of1000 images. In our experiments, we take each image as aquery image from each ofthe image categories and hence,a total of 1000 query images. For each query, we select thetop 100 results from the CBIR system to computeprecision, i.e. precision at 100, but in this paper we showonly top 25 results of some query image due to spacelimitation. Precision at 100 may be defined as theproportion of retrieved images that are relevant to thequery in the top 100 retrieved images. We tested it withfour different resolutions of images on COREL imagedatabase. Comparison shown in Figure 6 and Figure 7.

CBJR system Results ofDatabase 1

Fig.2a. Proposed CBIR system Results (Color & shape)system: 9 matches out of 12.

fig. 2b. Proposed CBIR system Results (Color & texture)system: 10 matches out of 12

NIET Journal of Engineering & Technology, Vol. 5, 2014 43

Fig. 2c. CLUE system Results: 8 matches out of 12

Fig. 2d. CLUE system Results: 4 matches out of 12

Fig.2: Comparison of results of the Proposed CB1R, CLUE andUFM for car category on image database I. The query image is the upper-left comer image of each block of images.

CBIR system Results ofDatabase 2

47 55

67 104

1&9 19 11

4/;5 41

17 22\:"20

337 415

Fig. 3b: Results of the Proposed CBIR system on imagedatabase3.CLUE and UFM not shown, The query image is the upper-leftcomer image of each block of images.

CBIR system Results ofDatabase 3

1,70 434 1,93

311 144 8

112 945 372362

261 21.)' 114

716 5619C".-0,.

Fig. 4a. Proposed CBIR system Results (Color & shape) system: 10matches out of25

400 417 )' 43 375

as 596 421 456

.1:'8 rz 275 69 .410

477 453 487 ::82

Xl 41) 32 3868'Ja 5:33 9J1 48t- 4:23

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Fig. 3a: Proposed CBIR system Results (Color & shape) system: IImatches out of25.

Fig. 4b. Proposed CBIR system Results (Color & Texture) system:16matches out of25

44 NIET Journal of Engineering & Technology, Vol. 5, 2014

TABLE I:Semantic Descriptor of the images of COREL Database resolution

256 X 384

Category No Category NameI African people and village2 Beach3 Building

4 Buses5 Peacock6 Elephants7 Flowers8 Horses9 Mountains and glaciers10 Food

TABLE 2:Comparison of performance of Proposed CBIR, CLUE, and UFM

using precision for each category of Database 4

m Category Precisi Precision at Precision at PredsioName on at lOO(CWE) lOO(Sllape- n at

IOO(U Color) IOO(fextFM) ure-

Color)1 People 0038 OA9 0.53 0.531 Beach 0.29 OJ.t OA2 OA33 Building 0.3.t 0.35 0.3" OA4 Buses 0.61 0.63 0.62 0.655 Dinosaurs 0.93 0.96 0.95 0.9~6 Elephants 0.23 0.28 OJ 0.327 Flowers 0.63 0" O.'.t O.-,8 Horses 0.62 0.- 0.'- O.-89 Mountains 0.25 0.28 0.3 0.310 Food OAS 0.6 0.6.t 0.65Average OAi3 0.538 0.56.t o.s-s

I 0 UFM D CLUE D Shape-Color CBIR D Tectture Color CBIR Il~ 75 !:'

0.9

0.8

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Fig. 5a. Proposed CBIR (shape-color) system Results:21 matches' out of25

19

51 75

:>1

7 37

3

Fig. 5b: Proposed CBIR (texture & color) system Results:19 matches out of 25

Table I shows that database of different categories andeach category have 100 images, hence the total images itcontains 1000.

2 3 4 5 6 7 8 9 10

Category 10

Fig. 6: Results of Comparison of proposed (Shape-Color &&Texture-Color) CBlR with CLUE and UFM on the Average Precision foreach category.

Table 2 shows that precision at 100 in both proposedapproaches in almost all categories are better than CLUEand UFM approaches that described in [6]. Fig. 7 showsthat on each resolution of image database proposedapproach outperform.

IV CONCLUSION

This paper proposed a content based image retrievalsystem based on unsupervised learning, where in, Icombine all the features values namely shape, color andtexture of an image for assigning a weight on differentimages (as a target images) in the image database withthreshold of 60%. I can take any value as a thresholdmeans minimum value of matching individual feature aswell as combined of both features value. I tested it onthreshold value 60 and we got the better results incomparison to given precision value by UFM and CLUE

NIET Journal of Engineering & Technology, Vol. 5, 2014

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45

TABLE 3:Average Precision on each resolution of image database

Image UF CLUE ICLUE ICLUE(T&C)Database M (S&C)

Da ta base 1(lS5XS .•) 0.28 0.55 0.69 0.76Database z(185X96) 0.36 0.37 0.42 0.44Database s(185X85) 0.39 0.-11 0.49 0.52Database .• 0.-17(38"X256) 3 0.538 0.56-1 0.575

o ICLUE (8 & C) 0 ICLUE (T & C) 0 ICLUE 0 UFM I0.8

0.7

a 0.6a1;; 0.5c; 0.40

. <;;'13 0.3~o,

0.2

0.1

0

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Category 10

Image Database (Resolution)

Fig. 7: Pictorial representation of Table 2 comparison shown on eachresolution of image database

4

on [6]. I experimented with a standard image database offour different resolution of image each databaseconsisting of approximately 1000 images to compare theperformance of the proposed systems by combining bothshape-color features and color-texture features with twoother existing CBIR systems. In our experiments, I usedEuclidean distance as the similarity measure forcomputing the similarity of images in the database with aquery image. Experimentally, I found that the proposedCBIR systems gives better results than the CLUE andUFM based CBIR systems in almost all categories of animage databases.

REFERENCES[I] Yixin Chen, Jmaes Z. Wang, and Robert Krovetz, "Contentbased

Image Retrieval by Clustering", International MultimediaConference, Proceedings of the 5th ACM SIGMM internationalworkshop on Multimedia information retrieval, pp. 193-200(2003).

[2] Yixin Chen, and James Z. Wang, "A Region-Based FuzzyFeature Matching Approach to Content-Based Image Retrieval",IEEE Transaction on Pattern Analaysis and MachineIntelligence, Vol. 24, No.9, pp. 1252- 1267 (September 2002).

[3] Yixin Chen, James Z. Wang, and Robert krovetz, "AnUnsupervised Learning Approach to Content-Based ImageRetrieval", IEEE Proc. Inter. Symposium on Signal Processingand ltsApplications, NEC Research Institute (2003).

[4] Yixin Chen, James Z. Wang, and Robert krovetz, "CLUE:Cluster-Based Retrieval of Images by Unsupervised learning",IEEE Transaction on Image Processing, Vol. 14, No.8, pp. 1187-1201 (August2005).

[5] Han, J. and Kuang, K., "Fuzzy color histogram and its use incolor image retrieval" IEEE Trans. Image Process. Vol. II, No.8., pp.944-952 (August 2000).

[6] Features for Visual Data," IEEE Trans. Knowledge and DataEngineering, vol. 14, no. 5, pp. 988-1002, (2002) .

[7] Jianbo Shi and Jitendra Malik, "Normalized Cuts and ImageSegmentation", IEEE Transaction on Pattern Analysis andMachine Intelligence, Vol. 22, No.8, pp. 888- 905 (August2000).

[8] A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang,"Image Classification for Content-Based Indexing," IEEETrans. Image Processing, vol. 10, no. I, pp. 117-130, (200 I).

[9] Josepf Fernandez, and Joan Aranda, "Image SegmentationCombining Region Depth and Object Features", icpr, vol. I,pp.1618, 15th International Conference on Pattern Recognition(ICPR'OO) - Volume 1,2000.

B Narasingh Patro received theMasters degree in the field of ComputerApplication from Biju PattniakTechnical University (BPUT), Odishain the year 2009. At present, he ispursuing Ph D in the field of ComputerScience from Monad University, Hapur,

Uttar Pradesh. As far as his academic experience isconcerned, he has been in teaching professional from2009. Since 2010, he is also working as a lecturer at NoidaInstitute of Engineering and Technology, Greater Noida,NCR, New Delhi. Before that he worked for SaiComputer Education, Berhampur for PGDCA, DCA .. Hehas good command over C, C++, JAVA, OperatingSystem, and RDBMS. He has published 3 internationalarticles in the field of computer science.

46 NIET Journal of Engineering & Technology, Vol. 5, 2014