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Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns
March 2011 (vol. 23 no. 3)
pp. 360-372
ASCII Text
x 
 
Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, Vincent S. Tseng, "Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 3, pp. 360-372, March, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.124,
author = {Ja-Hwung Su and Wei-Jyun Huang and Philip S. Yu and Vincent S. Tseng},
title = {Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {3},
issn = {1041-4347},
year = {2011},
pages = {360-372},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.124},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns
IS - 3
SN - 1041-4347
SP360
EP372
EPD - 360-372
A1 - Ja-Hwung Su,
A1 - Wei-Jyun Huang,
A1 - Philip S. Yu,
A1 - Vincent S. Tseng,
PY - 2011
KW - Content-based image retrieval
KW - relevance feedback
KW - query point movement
KW - query expansion
KW - navigation pattern mining.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Ja-Hwung Su, National Cheng Kung University, Taiwan
Wei-Jyun Huang, National Cheng Kung University, Taiwan
Philip S. Yu, University of Illinois at Chicago, Chicago
Vincent S. Tseng, National Cheng Kung University, Taiwan
Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

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Index Terms:
Content-based image retrieval, relevance feedback, query point movement, query expansion, navigation pattern mining.
Citation:
Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, Vincent S. Tseng, "Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 3, pp. 360-372, March 2011, doi:10.1109/TKDE.2010.124
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