Open Access   Article

Hybrid Features For Content Based Image Retrieval System

A.D. Mahajan1 , S. Chaudhary2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-10 , Page no. 11-15, Oct-2018


Online published on Oct 31, 2018

Copyright © A.D. Mahajan, S. Chaudhary . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A.D. Mahajan, S. Chaudhary, “Hybrid Features For Content Based Image Retrieval System”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.11-15, 2018.

MLA Style Citation: A.D. Mahajan, S. Chaudhary "Hybrid Features For Content Based Image Retrieval System." International Journal of Computer Sciences and Engineering 6.10 (2018): 11-15.

APA Style Citation: A.D. Mahajan, S. Chaudhary, (2018). Hybrid Features For Content Based Image Retrieval System. International Journal of Computer Sciences and Engineering, 6(10), 11-15.

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The “speedy progress in multimedia and imaging technology, the numbers of images uploaded and shared on the internet have increased. It leads to develop the highly effective image retrieval system to satisfy the human needs. The content-Based image retrieval (CBIR) system which retrieves the image based on the Low level features such as color, texture and shape which are not sufficient to describe the user’s high level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the most important notions in image retrieval are keywords, terms or concepts. Terms are used by humans to describe their information need and it also used by system as a way to represent images. Here in this paper different types of features their advantage and disadvantages are described. We have carried out comparative analysis of different techniques used in our system to determine best suitable technique to be used for our proposed system. We have analyze the our proposed system on large image dataset and our approach gives high precision and required less computations which proves efficiency of our system. In our proposed system we have evaluated the performance of our feature extraction techniques i.e. FCH and GWT using precision and recall metric and compared the result with existing feature extraction approaches i.e. color moment and GWT. Implementation results show that the feature extraction techniques for the proposed system are better than the existing techniques. SVM Classifier also gives good accuracy using these feature extraction” techniques.

Key-Words / Index Term

CBIR, Color Moment, Fuzzy Color Histrogram, Gabor Wavelate, Support Vector Machine


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