Image Retrieval Using Relevance Feedback Model

Image Retrieval Using Relevance Feedback Model

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-43 Number-2
Year of Publication : 2017
Authors : Neethu George, Akhil Paulose, Stephin Rachel Thomas

Citation 

Neethu George, Akhil Paulose, Stephin Rachel Thomas "Image Retrieval Using Relevance Feedback Model", International Journal of Engineering Trends and Technology (IJETT), V43(1),79-82 January 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images. This system proposes clustering based Relevance Feedback to achieve high effectiveness and efficiency. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, system makes use of the discovered navigation patterns and query refinement strategies. Usage of data mining techniques like Apriori algorithm, KNN approach, K-Means clustering not only improved the efficiency of the CBIR systems, but also improved the accuracy of the overall process. Content-based image retrieval with relevance feedback, based on the clustering algorithm is a novel approach.

 References

[1] Alami M.E, (2011) “A novel image retrieval model based on the most relevant features,” Elsevier Science , Knowledge-Based Systems,vol 24,pp 23–32.
[2] Ela Yildizer,Ali Metin Balci,Mohammad Hassan and Reda Alhajj, (2012) “ Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble,” Elsevier science , Expert Systems with Applications,vol 39,pp 2385–2396.
[3] Jun Yue, Zhenbo Li , Lu Liu and Zetian Fu, (2011) “Content-based image retrieval using color and texture fused features,” Elsevier Science, Mathematical and Computer Modelling ,vol 54, pp 1121–1127.
[4] Kim D.H and Chung C.W, (2003) “Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval,” Proc.ACM SIGMOD.

Keywords
Clustering, Relevance Feedback, indexing, Content based image retrieval, Frequent itemset mining.