6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

An Enhanced Approch for Content Based Image Retrieval

Author Affiliations

  • 1BIST, Bhopal, MP, INDIA

Res. J. Recent Sci., Volume 1, Issue (ISC-2011), Pages 415-428,(2012)


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.


  1. Gupta Neetesh, Singh R.K. and Dubey P.K., A NewApproach 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, Aretrieval pattern-based inter-query learning approachfor content-based image retrieval, Proceedings of 2010IEEE 17th International Conference on ImageProcessing, (2010)
  3. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, Department of Computer Science National TaiwanUniversity, Taipei 106, A Practical Guide to SupportVector Classication, Taiwan, Initial version, 2003,(2010)
  4. Ajitha Gladis K.P. and Ramar K., A Novel Method forContent Based Image Retrieval Using theApproximation of Statistical Features, MorphologicalFeatures and BPN Network, IEEE computer societyICCIMA, 148, 179-184 (2007)
  5. Liu P., Jia K., Wang Z. and Lv Z., A New andEffective Image Retrieval Method Based on CombinedFeatures, Proc. IEEE Int. Conf. on Image andGraphics, I, 786-790 (2007)
  6. Tienwei Tsai, Te-Wei Chiang and Yo-Ping Huan,Image Retrieval Approach Using Distance, ThresholdPruning, IEEE Trans. On Image Processing, 12, 241-249 (2007)
  7. Guoqiang Peter Zhang, Neural Networks forClassification, A Survey IEEE transactions on systems,man, and cybernetics—part C, applications andreviews, 30(4), (2000)
  8. Banzhaf W., Nordin P., Keller R.E. and Francone F.D., Genetic Programming: An Introduction, MorganKaufmann, San Francisco, CA, (1998)
  9. Richard M.D. and Lippmann R., Neural networkclassifiers estimate Bayesian a posteriori probabilities, Neural Comput., 3, 461–483, (1991)
  10. Duda P.O. and Hart P.E., Pattern Classification andScene Analysis, New York, Wiley (1973)