Adaptive region matching for region‐based image retrieval by constructing region importance index

2014 ◽  
Vol 8 (2) ◽  
pp. 141-151 ◽  
Author(s):  
Xiaohui Yang ◽  
Lijun Cai
Keyword(s):  
Image Retrieval ◽  
Importance Index ◽  
Region Matching

An Image Clustering and Feedback-Based Retrieval Framework

2012 ◽  
pp. 62-80
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Image Region ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

scholarly journals FReBIR: An image retrieval system based on fuzzy region matching

2009 ◽  
Vol 113 (6) ◽  
pp. 693-707 ◽  
Author(s):  
Sylvie Philipp-Foliguet ◽  
Julien Gony ◽  
Philippe-Henri Gosselin
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Region Matching ◽  
Image Retrieval System ◽  
Fuzzy Region

A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval

2019 ◽  
Vol 28 (3) ◽  
pp. 1191-1204 ◽  
Author(s):  
Zhimin Gao ◽  
Lei Wang ◽  
Luping Zhou
Keyword(s):  
Image Retrieval ◽  
Probabilistic Approach ◽  
Region Matching
Sign in / Sign up

Export Citation Format

Share Document