Publication Type:

Conference Paper

Source:

Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, p.704-711 (2013)

ISBN:

9781467350891

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84880123349&partnerID=40&md5=07278d0d009d64a7907ad14d92a2bfdd

Keywords:

CBIR, Communication, Content based image retrieval, Content based retrieval, Database systems, extensive features, Feature extraction, Gallium, Genetic algorithms, Image processing, Imaging systems, Medical imaging, Query processing, Search engines, SED, Shape features, Signal reconstruction, Signals and systems, Similarity measure, Squared euclidean distance

Abstract:

Nowadays, quick search and retrieval is needed in all kinds of growing database to find relevant details quickly. Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. Based on image content, CBIR extracts images that are relevant to the given query image from large image archives. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features such as shape, color, texture and homogeneity or high level features such as human perception. Most of the CBIR systems available in the literature extract only concise feature sets that limit the retrieval efficiency. In this paper, we are using Medical images for retrieval and the feature extraction is used along with color, shape and texture feature extraction to extract the query image from the database medical images. When a query image is given, the features are extracted and then the Genetic Algorithm-based similarity measure is performed between the query image features and the database image features. The Squared Euclidean Distance (SED) computes the similarity measure in determining the Genetic Algorithm fitness. Hence, from the Genetic Algorithm-based similarity measure, the database images that are relevant to the given query image are retrieved. The proposed CBIR technique is evaluated by querying different medical images and the retrieval efficiency is evaluated in the retrieval results. © 2013 IEEE.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@66a5f4c ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@319679de Through org.apache.xalan.xsltc.dom.DOMAdapter@e1af741; Conference Code:97801

Cite this Research Publication

Ba Syam, Victor, J. S. Rb, and Rao, Y. Sc, “Efficient similarity measure via genetic algorithm for content based medical image retrieval with extensive features”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 704-711.

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