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Content based Medical Image Retrieval with SVM Classification and Relevance Feedback

Sujata T Bhairnallykar, V. B. Gaikwad Published in Image Processing

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013
Year of Publication: 2013
© 2012 by IJAIS Journal
Series icwac Number 4
10.5120/icwac1318
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  1. Sujata T Bhairnallykar and V B Gaikwad. Article: Content based Medical Image Retrieval with SVM Classification and Relevance Feedback. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013 ICWAC(4):25-29, July 2013. BibTeX

    @article{key:article,
    	author = "Sujata T Bhairnallykar and V. B. Gaikwad",
    	title = "Article: Content based Medical Image Retrieval with SVM Classification and Relevance Feedback",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013",
    	year = 2013,
    	volume = "ICWAC",
    	number = 4,
    	pages = "25-29",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a day the content based image retrieval using relevance feedback are becoming a source of exact and fast retrieval. The idea of Content-based Image Retrieval (CBIR) using Relevance Feedback systems is to automatically extract image contents based on image features, i. e. color, texture, and shape and store in database and compare input query image feature with the features stored in database. Relevance feedback is applied to reduce the gap between high-level image semantics and low-level image features. Semantic gap is the difference between human perception of a concept and how it can be represented using machine level language.

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Keywords

Content-based image retrieval, Relevance feedback, SVM, CLD, EHD