Partitioning Based Clustering Method for Image Retrieval

Author(s):

Kiran P. Khandare , DESSCOET; Swati V. Kamble, DESSCOET; Shivani R. Jaiswal, DESSCOET; Nutan P. Khelkar, DESSCOET; A. V. Zade, DESSCOET

Keywords:

Feature Extraction Approaches, Partitioning Based Clustering

Abstract:

Clustering is a major technique used for grouping of numerical and image data in data mining and image processing applications. Clustering makes the job of image retrieval easy by finding the images as similar as given in the query image. The images are grouped together in some given number of clusters. Image data are grouped on the basis of some features such as color, texture, shape etc. contained in the images in the form of pixels. Content Based Image Retrieval (CBIR) is a collection of techniques for retrieving images from large database. The images are retrieved based on content. The term “content” demonstrate to color, texture and shapes. In this system, color and texture features are retrieved from images. The color features are obtained using Dynamic Color Distribution Entropy of Neighborhoods (D_CDEN). The texture features are obtained using Gray Level Co-occurrence Matrix (GLCM). The clustering technique is presented to solve the above problem. In this system, K-Means and Contribution based clustering techniques are used. The K-Means clustering algorithm optimizes only intra cluster similarity. Contribution based clustering enhance both intra and inter cluster similarity. The experimental results shows the comparison between average dispersion measures for both K-Means and Contribution based clustering.

Other Details:

Manuscript Id	:	IJSTEV4I10017
Published in	:	Volume : 4, Issue : 10
Publication Date	:	01/05/2018
Page(s)	:	22-26

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