Content Based Image Retrieval Using Hierachical and Fuzzy C-Means Clustering
Prof.S.Govindaraju, Dr.G.P.Ramesh Kumar; "Content Based Image Retrieval Using Hierachical and Fuzzy C-Means Clustering". International Journal of Computer Trends and Technology (IJCTT) V44(2):89-95, February 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Grouping images into semantically meaningful categories using low-level visual feature is a challenging and important problem in content based image retrieval. CBIR is a part of image processing. We know that with the development of the internet and the availability of image capturing devices such as digital cameras, image scanners, and size of the digital image collection is increasingly rapidly and hence there is a huge demand for effective image retrieval system. Normally CBIR is retrieving/ searching stored images from a collection by comparing features automatically extracted from the image themselves. The most common features used are mathematical measure is texture, color and shape. Clustered images are utilized by content-based image retrieval and querying system that requires effective query matching in large image database. Particularly, Inthis paper we are using HFCM Algorithm. It has the combinational advantage of both fuzzy and possiblistic approaches. The experimental results suggest that the proposed image retrieval technique results in better retrieval.
References
[1] Li, J., Wang, J. Z. and Wiederhold, G.,“Integrated Region Matching for ImageRetrieval,” ACM Multimedia, 2000, p. 147-156.
[2] Flickner, M., Sawhney, H., Niblack, W.,Ashley, J., Huang, Q., Dom, B., Gorkani, M.,Hafner, J., Lee, D., Petkovic, D., Steele, D.andYanker, P., “Query by image and videocontent: The QBIC system,” IEEE Computer,28(9), 1995,pp.23-32.
[3] Pentland, A., Picard, R. and Sclaroff S.,“Photo book: Content based manipulation of image databases”, International Journal ofComputer Vision, 18(3), 1996, pp.233–254.
[4] Smith, J.R., and Chang, S.F., “Single color extraction and image query,” In ProceedingIEEE International Conference on ImageProcessing, 1997, pp. 528–531.
[5] Gupta, A., and Jain, R., “Visual information retrieval,” Comm. Assoc. Comp. Mach., 40(5), 1997, pp. 70–79.
[6] M. Saadatmand-Tarzjan and H. A. Moghaddam, “A Novel Evolutionary Approach for Optimizing Content-Based Image Indexing Algorithms”, IEEE Transactions OnSystems, Man, And Cybernetics—Part B: Cybernetics, Vol. 37, No. 1, February 2007, pp. 139 153.
[7] N. Vasconcelos, “From Pixels to Semantic Spaces: Advances in Content-Based ImageRetrieval”,Computer Volume: 40, Issue: 7, 2007, pp. 20-26.
[8] N. Rasiwasia and N. Vasconcelos, “A Study of Query bySemantic Example”, 3rd International Workshop onSemantic Learning and Applications in Multimedia,Anchorage, June 2008, pp. 1-8.
[9] N. Rasiwasia, P. J. Moreno and N. Vasconcelos, “Bridging the Gap: Query by Semantic Example”, IEEETransactions On Multimedia, Vol. 9, No. 5, August 2007,pp. 923-938.
[10] S. Cheng, W. Huang, Y. Liao and D. Wu, “A ParallelCBIR Implementation Using Perceptual Grouping OfBlock-based Visual Patterns”,IEEE InternationalConference on Image Processing – ICIP, 2007, pp. V -161 - V - 164.
[11] D. Tao, X. Tang, and X. Li “Which Components are Important for Interactive Image Searching?”,IEEETransactions On Circuits And Systems For VideoTechnology, Vol. 18, No. 1, January 2008, pp. 3-11.
[12] Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trendsof the new age. In: MIR 2005: Proceedings of the 7th ACM SIGMM internationalworkshop on Multimedia information retrieval, pp. 253–262. ACM Press, New York(2005)
[13] Chang, H., Yeung, D.Y.: Kernel-based distance metric learning for content-basedimage retrieval. Image Vision Comput. 25, 695–703 (2007)
[14] Cz´uni, L., Csord´as, D.: Depth-based indexing and retrieval of photographic images.In: Garc´?a, N., Salgado, L., Mart´?nez, J.M. (eds.) VLBV 2003.LNCS, vol. 2849,pp. 76–83. Springer, Heidelberg (2003)
[15]. Zhang, D.S., Lu, G.: A comparative study on shape retrieval using fourier descriptorswith different shape signatures. In: Proc. of International Conference on Intelligent Multimedia and Distance Education (ICIMADE 2001), Fargo, ND, USA,pp. 1–9 (2001)
[16] Hiremath P.S. and JagadeeshPujari, “Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image”, International Journal of Image Processing, Volume (2): Issue(1).
[17] Dahlhaus, E.: “Parallel Algorithms for Hierarchical Clusterings and Applications to Split Decomposition and Parity Graph Recognition”. Journal of /Algorithms 36, 205-240(2000).
[18] P.Jeyanthi and V.JawaharSenthil Kumar, “Image Classification by K-Means Clustering“, Advances In Computational Sciences and Technology, Vol:3, No:1, pp. 1-8, 2010.
Keywords
Query, Hybrid Fuzzy C-Means, Content Based Image Retrieval.