scholarly journals A NEW CONTENT BASED IMAGE RETRIEVAL SYSTEM USING GMM AND RELEVANCE FEEDBACK

2014 ◽  
Vol 10 (2) ◽  
pp. 330-340 ◽  
Author(s):  
Shanmugapriya
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
Image Retrieval System

scholarly journals CONTENT BASED IMAGE RETRIEVAL SYSTEM BY FUSION OF COLOR, TEXTURE AND EDGE FEATURES WITH SVM CLASSIFIER AND RELEVANCE FEEDBACK

2018 ◽  
Vol 6 (9) ◽  
pp. 259-273
Author(s):  
Priyanka Saxena ◽  
Shefali
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Image Retrieval System ◽  
Combined Features ◽  
Edge Features ◽  
Color Texture

Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques.

scholarly journals An Efficient and Effective Image Retrieval System on the basis of (Feature, Matching Measure and sub-space) Selection

2018 ◽  
Vol 42 (2) ◽  
pp. 231-240
Author(s):  
Mawloud Mosbah
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Feature Matching ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
Review Of Literature ◽  
Or Efficiency ◽  
Feedback Information ◽  
Image Retrieval System

Since its appearance as a research field, Content-based Image Retrieval (CBIR) system has increasingly received an important attention. Review of literature reveals that the efforts put, up to now, in the field address either effectiveness or efficiency. In this paper, we address both accuracy and efficiencythrough introducing an efficient and an effective image retrieval approach based on feature, matching measure and sub-spaceselection. The selection relies on relevance feedback information injected by the user. The approach is tested on Corel-1Kimages database. The obtained results are very promising.

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