Region-based Image Retrieval Using Probabilistic Feature Relevance Learning

2001 ◽  
Vol 4 (2-3) ◽  
pp. 174-184 ◽  
Author(s):  
Jing Peng ◽  
ByoungChul Ko ◽  
Hyeran Byun
Keyword(s):  
Image Retrieval ◽  
Feature Relevance ◽  
Relevance Learning

Hybrid textual-visual relevance learning for content-based image retrieval

2017 ◽  
Vol 48 ◽  
pp. 367-374 ◽  
Author(s):  
Chaoran Cui ◽  
Peiguang Lin ◽  
Xiushan Nie ◽  
Yilong Yin ◽  
Qingfeng Zhu
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Relevance Learning

A FEATURE RELEVANCE ESTIMATION METHOD FOR CONTENT-BASED IMAGE RETRIEVAL

2011 ◽  
Vol 10 (05) ◽  
pp. 933-961 ◽  
Author(s):  
HOSSEIN AJORLOO ◽  
ABOLFAZL LAKDASHTI
Keyword(s):  
Image Retrieval ◽  
Analytical Approach ◽  
Optimization Problem ◽  
Estimation Method ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Constrained Optimization Problem ◽  
Short Term ◽  
Feature Relevance

Feature relevance estimation is one of the most successful techniques used for improving the retrieval results of a content-based image retrieval (CBIR) system based on users' feedbacks. In this class of approaches, the weights of the feature elements (FEs) are adjusted based on the relevance feedbacks (RFs) given by the users to reduce the so-called semantic gap in the underlying CBIR system. An analytical approach is proposed in this paper to convert the users' feedbacks to the appropriate FE weights by solving a constrained optimization problem. Experiments on a set of 11,000 images from the Corel database show that the proposed approach outperforms other existing short-term RF approaches reported in the literature. The proposed approach is also incorporated in two long-term RF methods and enhanced their performance.

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