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An Effective Combined Feature For Web Based Image Retrieval
[Full Text]
AUTHOR(S)
H.M.R.B Herath, Y.P.R.D Yapa
KEYWORDS
Index Terms: Content Based Image Retrieval, Computer Vision, Image Retrieval, Image Processing, Web Based Image Retrieval
ABSTRACT
Abstract: Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features: Colour moments, Colour coherence vector and Colour Correlogram, and three texture features: Grey Level Co-occurrence matrix, Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes, images of flowers etc. Also, images which have a similar colour and texture distribution were retrieved as similar, even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants to retrieve images which are aesthetically similar and not interested in semantic similarity.
REFERENCES
[1] J. Eakins and M. Graham, “Content-based Image Retrieval,” A Report to the JISC Technology Applications Programme, Institute for image database research, University of Northumbria, Newcastle, UK, 1999.
[2] Ahmed J. Afifi and Wesam M. Ashour, “Image Retrieval Based on Content Using Colour Feature,” ISRN Computer Graphics, vol. 2012, Article ID 248285, 11 pages, 2012. doi:10.5402/2012/248285
[3] B.S. Manjunathi and W.Y. Ma, "Texture Features for Browsing and Retrieval of Image Data", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI - Special issue on Digital Libraries), vol. 18, no. 8, pp. 837-42, Aug. 1996.
[4] Neetu Sharma. S, Paresh Rawat S and jaikaran Sing S, “Efficient CBIR Using Colour Histogram Processing”, Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011.
[5] H.B.D. Himali, C.P.S. Kaluarachchi, W.M.D. Sajini, and D.S. Deegalla, “A Web-Based Content-Based Image Retrieval System”, Proceedings of the Second Engineering Students’ Conference at Peradeniya 2013 (ESCaPe’13)
[6] Tamer Mehyar, Jalal Omer Atoum, "An Enhancement on Content-Based Image Retrieval using Colour and Texture Features", Journal of Emerging Trends in Computing and Information Sciences, Vol.3, No.4, April 2012.
[7] Minakshi Kaushik, Rahul Sharma, Ankit Vidhyarthi, "Analysis of Spatial Features in CBIR System", International Journal of Computer Applications (0975 – 8887), Vol.54, No.17, September 2012
[8] Pass, Greg, Ramin Zabih, and Justin Miller. "Comparing Images Using Colour Coherence Vectors." Proceedings of the fourth ACM international conference on Multimedia. ACM, 1997.
[9] P. S. Hiremath and Jagadeesh Pujari, “Content Based Image Retrieval based on Colour, Texture and Shape Features Using Image and its Complement”, International Journal of Computer Science and Security, Volume (1) : Issue (4).
[10] Soman, Sagar, et al. "Content Based Image Retrieval Using Advanced Colour and Texture Features." International Conference in Computational Intelligence (ICCIA), 2012.
[11] M. Narayana and Subhash Kulkarni, “Content Based Image Retrieval Using Sketches”, Proceedings of ICAdC, AISC 174, pp. 1117–1123.
[12] Wei-Ying Ma1, B. S. Manjunath, “NeTra: A Toolbox for Navigating Large Image Databases”, Multimedia Systems 7:,pp.184–198 ,1999.
[13] Mezaris, Vasileios, Ioannis Kompatsiaris, and Michael G. Strintzis. "An Ontology Approach to Object-based Image Retrieval." Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on. Vol. 2. IEEE, 2003.
[14] Zhang, Lei, Fuzong Lin, and Bo Zhang. "Support Vector Machine Learning for Image Retrieval." Image Processing, 2001. Proceedings. 2001 International Conference on. Vol. 2. IEEE, 2001.
[15] Rui, Yong, Thomas S. Huang, and Sharad Mehrotra. "Content-based Image Retrieval With Relevance Feedback in MARS." Image Processing, 1997. Proceedings., International Conference on. Vol. 2. IEEE, 1997.
[16] S. Mangijao Singh, K. Hemachandran, “Content-Based Image Retrieval Using Colour Moment and Gabor Texture Feature”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012.
[17] Ramakrishna Reddy, Eamani et al, “Content-Based Image Retrieval Using Support Vector Machine in Digital Image Processing Techniques”, International Journal of Engineering Science and Technology (IJEST), Vol. 4 No.04 April 2012.
[18] Howarth, Peter, and Stefan Rüger. "Evaluation of Texture Features for Content-based Image Retrieval." Image and Video Retrieval. Springer Berlin Heidelberg, 2004. 326-334.
[19] Anantharatnasamy Pratheep, Kaavya Sriskandaraja, Vahissan Nandakumar, and Sampath Deegalla. "Fusion of Colour, Shape and Texture Features for Content Based Image Retrieval." In Computer Science & Education (ICCSE), 2013 8th International Conference on, pp. 422-427. IEEE, 2013.
[20] Krishnamachari Santhana, and Mohamed Abdel-Mottaleb. “Hierarchical Clustering Algorithm for Fast Image Retrieval.” Electronic Imaging'99. International Society for Optics and Photonics, 1998.
[21] Huang Jing, S. Ravi Kumar, Mandar Mitra, Wei-Jing Zhu, and Ramin Zabih. "Image Indexing Using Colour Correlograms." In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pp. 762-768. IEEE, 1997.
[22] Afifi, Ahmed J., and Wesam Ashour. "Image Retrieval Based on Content Using Colour Feature: Colour Image Processing and Retrieving." (2011).
[23] Thakore, Darshak G., and A. I. Trivedi. "Content Based Image Retrieval Techniques–Issues, Analysis and the State of the Art." BVM Engineering College, Gujarat (2010).
[24] Selvarajah, S., and S.R. Kodituwakku. "Performance Evaluation of Shape Analysis Techniques.", ARPN Journal of Systems and Software, Vol.1, No. 1,April 2011.
[25] Tamura, Hideyuki, Shunji Mori, and Takashi Yamawaki. "Textural Features Corresponding to Visual Perception." Systems, Man and Cybernetics, IEEE Transactions on 8.6 (1978): 460-473.
[26] Baraldi, Andrea, and Flavio Parmiggiani. "An Investigation of the Textural Characteristics Associated With Gray Level Cooccurrence Matrix Statistical Parameters." Geoscience and Remote Sensing, IEEE Transactions on 33.2 (1995): 293-304.
[27] Niblack, Carlton W., et al. "QBIC Project: Querying Images by Content, Using Colour, Texture, and Shape." IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology. International Society for Optics and Photonics, 1993.
[28] Howarth, Peter, et al. "Visual Features for Content-Based Medical Image Retrieval." Proceedings of Cross Language Evaluation Forum (CLEF) Workshop. 2004.
[29] Long, Fuhui, Hongjiang Zhang, and David Dagan Feng. "Fundamentals of Content-Based Image Retrieval." Multimedia Information Retrieval and Management. Springer Berlin Heidelberg, 2003. 1-26.
[30] S Selvarajah, S Kodituwakku. “A Combination of Colour Moments and Colour Coherence Vector for Image Retrieval.” ARPN J Syst Softw 1, 12-18.
[31] S.R. Kodituwakku, S Selvarajah. “Combined Feature Descriptor for Content Based Image Retrieval.” Indian Journal of Computer Science and Engineering 1 (3), 207-211
[32] S.R. Kodituwaku, M.I.M. Fazeen. An offline fuzzy based approach for iris recognition with enhanced feature detection, Advanced techniques in computing sciences and software engineering, Springer Science & Business Media, 2010. Pp : 39 -44.
[33] S. Selvarajah, S.R. Kodituwakku. “Comparison of Colour Features for Image Retrieval.” International Journal of Latest Trends in Computing 2 (1). Pp.207-211, 2011
[34] S. Selvarajah, S.R. Kodituwakku. “Analysis and comparison of texture features for content based image retrieval.” International Journal of Latest Trends in Computing, 2(1), pp. 108 – 113, 2011.
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