CBIR through CDH using Query by Group

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-28 Number-1
Year of Publication : 2015
Authors : Anjali Sharma, Ajay Kr. Singh


Anjali Sharma, Ajay Kr. Singh "CBIR through CDH using Query by Group". International Journal of Computer Trends and Technology (IJCTT) V28(1):21-27, October 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In recent years we have seen a great pace in the research related to content-based image retrieval. This paper presents a technique for image retrieval based on the color and edge orientation as the significant features for retrieval of similar images. The paper uses a form of Histogram known as Color Difference Histogram (CDH), which may be regarded as an improved version of a Multi-Texton Histogram (MTH). The given histogram is different from the other existing histograms as it does not count the frequency of the number of pixels. The main essence of CDH is that it takes into account the perceptually uniform color difference between two points under different background in accordance with the color and the edge orientations. Here, both the color and texture based features are employed in a single algorithm to get better performance. In order to achieve the perceptual color difference, we exploit the L*a*b color space. The following color space removes the drawbacks of the most commonly used color space of retrieval purposes such as RGB color space as its components are highly correlated. Further in this paper we extend the concept of Query by example to Query-By-Group.

[1] Y. Rui, T.S. Huang, and S.-F. Chang, “Image retrieval: Current techniques, promising directions, and open issues,” Journal of visual communication and image representation, vol. 10, 1999, pp. 39–62.
[2] .-K. Chang and A. Hsu, “Image information systems: where do we go from here?,” IEEE transactions on Knowledge and Data Engineering, vol. 4, 1992, pp. 431–442.
[3] Flickner, M., Sawhney, H. and et al. “Query by Image and Video content: The QBIC system”, In IEEE Computer, Vol.28, No.9, pp. 23-32, September 1995.
[4] Sticker, M. and Orengo, M., Similarity of Color Images. In Proceedings of SPIE, Vol. 2420 (Storage and Retrieval of Image and Video Databases III), SPIE Press, Feb. 1995.
[5] Y.-K. Chan and C.-Y. Chen, “Image retrieval system based on color-complexity and color-spatial features,” Journal of Systems and Software, vol. 71, 2004, pp. 65–70.
[6] J.M. Fuertes, M. Lucena, N. Perez de la Blanca, and J. Chamorro-Martnez, “A scheme of colour image retrieval from databases,” Pattern Recognition Letters, vol. 22, 2001, pp. 323– 337.
[7]. J. Huang, S.R. Kumar, M. Mitra, et al., “Image indexing using color correlograms”, in: IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 762–768.
[8]. H. Tamura, S. Mori, T. Yamawaki, Texture features corresponding to visual perception, IEEE Transactions on Systems, Man, and Cybernetics 8 (6) 1978, pp. 460–473.
[9]. G. Cross, A. Jain, Markov random field texture models, IEEE Transactions on Pattern Analysis and Machine Intelligence 5 (1) (1983) pp. 25–39.
[10] B.S. Manjunathi, W.Y. Ma, “Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence” 18 (8) 1996 pp. 837–842.
[11] C. Palm, “Color texture classification by integrative cooccurrence matrices”, Pattern Recognition 37 (5) 2004 pp. 965– 976.
[12] G.-H. Liu, L. Zhang, et al., “Image retrieval based on multitexton histogram”, Pattern Recognition 43 (7) 2010 pp. 2380– 2389.
[13] J. Luo, D. Crandall, Color object detection using spatialcolor joint probability functions, IEEE Transactions on Image Processing 15 (6) (2006) 1443–1453.
[14] J. Mao, A.K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models”, Pattern Recognition, Vol. 25, No.2, pp. 173-188, 1992.
[15] W. Burger, M.J. Burge, “Principles of Digital image processing: Core Algorithms”, Springer, 2009.
[16] S. Kastner and L.G. Ungerleider, “The neural basis of biased competition in human visual cortex”, Neuropsychologia 39 (12) 2001 pp. 1263–1276.
[17] M.S. Livingstone and D.H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex”, The Journal of Neuroscience 4 (1) 1984 pp. 309–356.
[18] W. Burger, M.J. Burge, Principles of Digital image processing: Core Algorithms, Springer, 2009.
[19] R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edition, Prentice- Hall, 2007.
[20] Julesz, “Textons, the elements of texture perception and their interactions”, Nature 290 (5802) 1981 pp. 91–97.
[21] C.F. Lam, M.C. Lee, Video segmentation using color difference histogram, in: Proceeding of the International Workshop on Multimedia Information Analysis and Retrieval, London, UK, 1998.
[22] G.N.Lance and W.T.Williams, Mixed-data classificatory programs I agglomerative systems, Australian Computer Journal 1 (1) 1967 pp.15–20.

Image retrieval, CDH, L*a*b color space, Query by example and Query by Group.