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  An Expert Technique for Content-Based Color Image Retrieval  
  Authors : Subha Vaiapury
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Recently, as web and various databases contain a large number of images, CBIR (content-based image retrieval) are greatly used. This paper proposes an image retrieval system using color-spatial information from the applications. First, we suggest two kinds of the indexing keys to prune away the irrelevant images to given query images using MCS (Major Color Set) and DBS (Distribution Block Signature). MCS’s are related to color information while, DBS’s are related to spatial information respectively. After successively applying these filters to a large database, we get only a small amount of high potential candidates that is somewhat similar to that of query images. We propose to use QM (quad modeling) method to set the initial weight of 2-dimensional cell in the query image according to each major color and retrieve more similar images through similarity association function associated with the weights. Finally, we evaluated the system’s efficiency by statistically how many images were expected to be filtered out during the first and second filtering processes.


Published In : IJCSN Journal Volume 6, Issue 3

Date of Publication : June 2017

Pages : 351-355

Figures :06

Tables : --


V.Subha : is currently working as an Assistant professor in RGCET in department of computer science, puducherry, India. Her research interests include image, video processing, computer vision, big data and networks.


Content-Based Image Retrieval, Major Color Set, Global Color Signature, Distribution Block Signatures, Quad Modeling, Hue Saturation Value

In this work, we used signature based on the color, spatial approach for addressing the content based image retrieval problem. The chosen MCS, GCS and DBS color signature in the approach is used for efficient retrieval of an image. Further, in this work, we used an HSV color model to represent an image. We have studied and implemented the system up to the first step of QM modeling in VB.Net Environment. In future, similarity measure for QM matrix can be calculated to better capture the amount of overlap between query and database images. Further for accurate retrieval, the relevance feedback approach can be incorporated and performance of the system using precision and recall measures can be used for evaluation.


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