A Robust Image Representation and Learning Method using SVM for Image Retrieval

A Robust Image Representation and Learning Method using SVM for Image Retrieval
Authors : Shoaib Masroor . Ts, Prof. Arvind Kumar Sharma
Publication Date: 15-03-2017


Author(s):  Shoaib Masroor . Ts, Prof. Arvind Kumar Sharma

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 03 , March - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS030204


Multimedia applications are increasing rapidly and a large number of digital images are stored in database. For the effective retrieval of the desired images from a huge image database study of a content-based image retrieval (CBIR) technique has become an important research issue. In this proposed work, image retrieval is done through color and texture feature extraction. For feature extraction different algorithms are used like color auto correlogram, HSV (Hue Saturation Value) histogram and color moments. Also, texture features like mean square energy, mean amplitude of 2D wavelet component and standard deviation of wavelet coefficients. Features of the query image and the database images are classified and compared using support vector machine and similarity measures. Features are compared based on pair wise euclidean distance between query image and database image by various methods such as L1, cityblock, minkowski, chebychev, cosine, correlation and spearman.


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