Performance Analysis of Minimum Distance Classifier and Clustering Algorithm in CBIR


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Content-based image retrieval (CBIR) system can be used to effectively and precisely retrieve the desired images from a large image database, and the development has become an important research issue.Statistical methods like, gray level co-occurrence matrix (GLCM) and the autocorrelation function are used to extract texture feature. Region-based methods utilize information from both boundaries and interior regions of the shape. Shape features like perimeter, area, centroid, circularity, solidity based on region can be extracted in the feature space. Similar images can be retrieved using minimum distance classifiers with and without clustering algorithm .Time complexity and the retrieval efficiency has been analyed and compared on both the methods. The experiments have been conducted on MPEG-7 dataset.



Edited by:

R. Edwin Raj, M. Marsaline Beno and M. Carolin Mabel




J. A. Rose and C. C. Seldev, "Performance Analysis of Minimum Distance Classifier and Clustering Algorithm in CBIR", Applied Mechanics and Materials, Vol. 626, pp. 14-19, 2014

Online since:

August 2014




* - Corresponding Author

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