Image Retrieval Using Relevance Feedback Model
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Neethu George, Akhil Paulose, Stephin Rachel Thomas
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
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.
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Clustering, Relevance Feedback, indexing, Content based image retrieval, Frequent itemset mining.