Texture Based Image retrieval using Human interactive Genetic Algorithm

S.Sreenivas Rao, K.Ravi Kumar, G. Lavanya Devi

Abstract


Content-based image retrieval has been keenly calculated in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval has become one of the liveliest researches in the past few years. As earlier, we were using the text-based approach where it initiate very boring and hard task for solving the purpose of image retrieval. But the CBIR is the method where there are several methodologies are available and the task of image retrieval becomes well easier. In this, there are specific effective methods for CBIR are discussed and the relative study is made. However most of the proposed methods emphasize on finding the best representation for diverse image features. Here, the user-oriented mechanism for CBIR method based on an interactivegenetic algorithm (IGA) is proposed. Color attributes likethe mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histograms of an image are too considered as the texture features.

Keywords


content-based image retrieval (CBIR), human–machine interaction, interactive genetic algorithm (IGA), color attributes, low-level descriptors.

References


M. Antonelli, S. G. Dellepiane, and M. Goccia, “Design and implementation of Web based systems for image segmentation and CBIR,” IEEETrans.Instrum. Meas., vol. 55, no. 6, pp. 1869–1877, Dec. 2006.

N. Jhanwar, S. Chaudhuri, G. Seetharaman, and B. Zavidovique, “Content based image retrieval using motif cooccurrence matrix,” Image Vis.Comput., vol. 22, no. 14, pp. 1211–1220, Dec. 2004.

J. Han, K. N. Ngan, M. Li, and H.-J.Zhang, “A memory learning framework for effective image retrieval,” IEEE Trans. Image Process., vol. 14,no. 4, pp. 511–524, Apr. 2005.

H. Takagi, S.-B. Cho, and T. Noda, “Evaluation of an IGA-based image retrieval system using wavelet coefficients,” in Proc. IEEE Int. Fuzzy Syst.Conf., 1999, vol. 3, pp. 1775–1780.

H. Takagi, “Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation,” Proc. IEEE, vol. 89, no. 9,pp. 1275–1296, Sep. 2001.

S.-B. Cho and J.-Y.Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Trans. Syst., Man, Cybern. A, Syst.,Humans, vol. 32, no. 3, pp. 452–458, May 2002.

Y. Liu, D. Zhang, G. Lu, andW.-Y.Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognit., vol. 40, no. 1,pp. 262–282, Jan. 2007.

A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain,“Content-based image retrieval at the end of the early years,” IEEE Trans.Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec. 2000.

S. Antani, R. Kasturi, and R. Jain, “A survey of the use of pattern recognition methods for abstraction, indexing and retrieval of images and video,”PatternRecognit., vol. 35, no. 4, pp. 945–965, Apr. 2002.

X. S. Zhou and T. S. Huang, “Relevance feedback in content-based image retrieval: Some recent advances,” Inf. Sci., vol. 148, no. 1–4, pp. 129–137,Dec. 2002.

H.-W. Yoo, H.-S.Park, and D.-S.Jang, “Expert system for color image retrieval,” Expert Syst. Appl., vol. 28, no. 2, pp. 347–357, Feb. 2005.

T.-C. Lu and C.-C. Chang, “Color image retrieval technique based on color features and image bitmap,” Inf. Process. Manage., vol. 43, no. 2,pp. 461–472, Mar. 2007.

A. Vadivel, S. Sural, and A. K. Majumdar, “An integrated color and intensity co-occurrence matrix,” Pattern Recognit ].Lett., vol. 28, no. 8,pp. 974–983, Jun. 2007.

M. H. Pi, C. S. Tong, S. K. Choy, and H. Zhang, “A fast and effective model for wavelet sub band histograms and its application in texture image retrieval,” IEEE Trans. Image Process., vol. 15, no. 10, pp. 3078–3088,Oct. 2006.

M. Kokare, P. K. Biswas, and B. N. Chatterji, “Texture image retrieval using new rotated complex wavelet filters,” IEEE Trans. Syst., Man, Cybern.B, Cybern., vol. 35, no. 6, pp. 1168–1178, Dec. 2005.

M. Pi and H. Li, “Fractal indexing with the joint statistical properties and its application in texture image retrieval,” IET Image Process., vol. 2,no. 4, pp. 218–230, Aug. 2008.

S. Liapis and G. Tziritas, “Color and texture image retrieval using chromaticity histograms and wavelet frames,” IEEE Trans.Multimedia, vol.6,no. 5, pp. 676–686,Oct.2004.

Y. D. Chun, N. C. Kim, and I. H. Jang, “Content-based image retrieval using multiresolution color and texture features,” IEEE Trans. Multimedia,vol. 10, no. 6, pp. 1073–1084, Oct. 2008.

S.-B. Cho, “Towards creative evolutionary systems with interactive genetic algorithm,” Appl. Intell., vol. 16, no. 2, pp. 129–138, Mar. 2002.

S.-F. Wang, X.-F.Wang, and J. Xue, “An improved interactive genetic algorithm incorporating relevant feedback,” in Proc. 4th Int. Conf. Mach.Learn.Cybern., Guangzhou, China, 2005, pp. 2996–3001.

M. Arevalillo-Herráez, F. H. Ferri, and S. Moreno-Picot, “Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval,” Appl. Soft Comput., vol. 11, no. 2, pp. 1782–1791, Mar. 2011,DOI: 10.1016/j.asoc.2010.05.022.

S. Shi, J.-Z.Li, and L. Lin, “Face image retrieval method based on improved IGA and SVM,” in Proc. ICIC, vol. 4681, LNCS, D.-S.Huang,L. Heutte, and M. Loog, Eds., 2007, pp. 767–774.


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