Image retrieval is one of the most interesting andfastest growing research areas in the field of digital image processing as well as for the information retrieval from web contents. In mostContent-Based Image Retrieval (CBIR) systems, an image isrepresented by a set of different level of visual features, by which can manage large databases. Most of the popular database removes the high-level semantic information.Here we this paper an novel approach named content based image retrieval using two tire architecture, to maintaining and reducing the exists gap between high-level and low-level features, where SVM classification is used in first layer after feature generation, therefore proceed it output as input into the second layer, where the resultant images again classified and will produce more accurate result while retrieval. And finally most similar images will retrieved according to the user specified query image.

Digital Image Processing, SVM, Fuzzy, CBIR, KNN, Semantic gap, colour feature

[1] Hongbao Cao, Hong-Wen Deng, and Yu-Ping Wang “Segmentation of M-FISH Images for Improved Classification of Chromosomes With an Adaptive Fuzzy C-means Clustering Algorithm” in IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 20, NO. 1, February 2012.
[2] LexiaoTian, DequanZheng and Conghui Zhu “Research on Image Classification Based on a Combination of Text and Visual Features” in Eighth International Conference on Fuzzy Systems and Knowledge Discovery 2011.
[3] D. Tao, X. Tang, X. Li, and X.Wu, “Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 7, pp. 1088–1099, Jul. 2007.
[4] G. Guo, A. K. Jain, W. Ma, and H. Zhang, “Learning similarity measure for natural image retrieval with relevance feedback,” IEEE Trans. Neural Network., vol. 13, no. 4, pp. 811–820, Jul. 2002.
[5] SaurabhAgrawal, Nishchal K Verma, PrateekTamrakar, PradipSircar, “Content based color image classification using SVM”, Department of Electrical Engg., IIT, Kanpur, India, 2011 8th international conference on information technology.
[6] Oren Boiman, Eli Shechtman, Michal Irani, “In defense of Nearest-Neighbor based Image Classification”, WIS Rehovot, ISRAEL.
[7] SerafeimMoustakidis, GiorgosMallinis, Nikos Koutsias, John B. Theocharis, “SVM-Based Fuzzy Decision Trees for Classification.
of High Spatial Resolution Remote Sensing Images” Member IEEE.
[8] Lining Zhang, Student Member, IEEE, Lipo Wang, Senior Member, IEEE, Weisi Lin, Senior Member, IEEE, “Semi supervised biased maximum margin analysis for interactive image retrieval”, IEEE transaction of image processing, Vol.21, No.4, April 2012.
[9] W. M. Smeuldes, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content based image retrieval at the end of the early years,” IEEE Trans. Pattern And Mach Intell, vol.21, No.4,pp. 1349-1380, Dec.2002.
[10] PoojaKamavisdar, SonamSaluja, SonuAgrawal. “A survey onimage classification approaches and techniques”, Department of Computer Science & Applications, SSCST, Bhilai, India, IJARCCE, Vol.2, Issue.1, Jan 2013.
[11] MostafaSabzekar, Mohammad Ghasemigol, Mahmoud Naghibzadeh, H. S. Yazdi, “Improved DAG SVM a new method for multiclass svm classification”, Department of computer science & Engineering, Ferdowsi University of Mashhad, Iran ICAI’09I.
[12] JipsaKurian, V. Karunakaran, “A survey on image classification methods”, Department of Computer Science, Karunya University, Coimbatore, India, IJARECE, Volume 1, Issue.4, Oct 2012.
[13] Swati Agarwal, A. K. Verma, Preetvanti Singh “Content Based Image Retrieval using Discrete Wavelet Transform and Edge Histogram Descriptor”,International Conference on Information Systems and Computer Networks IEEE, 2013
[14] Sreena P. H., David Solomon George “Content Based Image Retrieval System with Fuzzified Texture Similarity Measurement”,Department of ECE, Govt. Rajiv Gandhi Institute of technology Kottayam, India, International Conference on Control Communication and Computing 2013
[15] http://www.jars1974.net/pdf/12_Chapter11.pdf
[16] Machine Bounthanh, Kazuhiko Hamamoto, BoonwatAttachoo, ThaBounthanh “Content-Based Image Retrieval System Based on Combined and Weighted Multi-Features” King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520, Thailand, 13th International Symposium on Communications and Information Technologies 2013.
[17] KhadidjaBelattar, SihemMostefai “CBIR using Relevance Feedback:Comparative Analysis and Major Challenges” Computer Science Department MISC Laboratory Mentouri University Constantine , Algeria, 5th International Conference on Computer Science and Information Technology 2013.
[18] Jisha.K.P, Thusnavis Bella Mary. I, Dr.A.Vasuki “An Image Retrieval Technique Based on Texture Features using Semantic Properties” International Conference on Signal Processing, Image Processing and Pattern Recognition 2013.
[19] Qian Du, “Unsupervised real time constrained linear discriminate analysis to hyper spectral image classification”, Department of Electrical & Computer Engg, Mississippi state university, USA, www.sciencedirect.com, Pattern Reorganization (2005) 361-368.