Online Image Retrieval System Using Long Term Relevance Feedback

2006 ◽  
pp. 422-431 ◽  
Author(s):  
Lutz Goldmann ◽  
Lars Thiele ◽  
Thomas Sikora
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Image Retrieval System

Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method

2010 ◽  
Vol 14 (1) ◽  
pp. 34-38 ◽  
Author(s):  
M. Rahmat Widyanto ◽  
◽  
Tatik Maftukhah ◽  
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Euclidean Distance ◽  
Retrieval System ◽  
User Feedback ◽  
Image Similarity ◽  
Modification Method ◽  
Image Retrieval System ◽  
Membership Value ◽  
Image Computation

Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.

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