Research Journal of Recent Sciences ________________________________________________ ISSN 2277 - 2502 Vol. 1(ISC-2011), 415-418 (2012) Res.J.Recent.Sci. Mini Review Paper An Enhanced Approch for Content Based Image Retrieval Patheja P.S., Waoo Akhilesh A. and Maurya Jay Prakash BIST, Bhopal, MP, INDIA Available online at: www.isca.in (Received 6th October 2011, revised 5th January 2012, accepted 25th January 2012) Abstract Image classification is perhaps the most important part of digital image analysis. Retrieval pattern-based learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. In this new approach we use neural network based pattern learning to achieve effective classification and with neural network we use decision tree algorithm to make less complex mining of images. That approach is more effective and efficient way for image retrieval. Keywords: pattern-based learning, image retrieval, neural network. References 1. Gupta Neetesh, Singh R.K. and Dubey P.K., A New Approach for CBIR Feedback based image classifier, International Journal of Computer Applications (0975 – 8887) 14(4), (2011) 2. Gilbert Adam D., Chang Ran, and Xiaojun Qi, A retrieval pattern-based inter-query learning approach for content-based image retrieval, Proceedings of 2010 IEEE 17th International Conference on Image Processing, (2010) 3. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin Department of Computer Science National Taiwan University, Taipei 106, A Practical Guide to Support Vector Classication, Taiwan, Initial version, 2003, (2010) 4. Ajitha Gladis K.P. and Ramar K., A Novel Method for Content Based Image Retrieval Using the Approximation of Statistical Features, Morphological Features and BPN Network, IEEE computer society ICCIMA, 148, 179-184 (2007) 5. Liu P., Jia K., Wang Z. and Lv Z., A New and Effective Image Retrieval Method Based on Combined Features, Proc. IEEE Int. Conf. on Image and Graphics, I, 786-790 (2007) 6. Tienwei Tsai, Te-Wei Chiang and Yo-Ping Huan, Image Retrieval Approach Using Distance, Threshold Pruning, IEEE Trans. On Image Processing, 12, 241- 249 (2007) 7. Guoqiang Peter Zhang, Neural Networks for Classification, A Survey IEEE transactions on systems, man, and cybernetics—part C, applications and reviews, 30(4), (2000) 8. Banzhaf W., Nordin P., Keller R.E. and Francone F.D., Genetic Programming: An Introduction, Morgan Kaufmann, San Francisco, CA, (1998) 9. Richard M.D. and Lippmann R., Neural network classifiers estimate Bayesian a posteriori probabilities, Neural Comput., 3, 461–483, (1991) 10. Duda P.O. and Hart P.E., Pattern Classification and Scene Analysis, New York, Wiley (1973)