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A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
April 2006 (vol. 18 no. 4)
pp. 509-524
ASCII Text
x 
 
Steven C.H. Hoi, Michael R. Lyu, Rong Jin, "A Unified Log-Based Relevance Feedback Scheme for Image Retrieval," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 4, pp. 509-524, April, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2006.53,
author = {Steven C.H. Hoi and Michael R. Lyu and Rong Jin},
title = {A Unified Log-Based Relevance Feedback Scheme for Image Retrieval},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {18},
number = {4},
issn = {1041-4347},
year = {2006},
pages = {509-524},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.53},
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 - A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IS - 4
SN - 1041-4347
SP509
EP524
EPD - 509-524
A1 - Steven C.H. Hoi,
A1 - Michael R. Lyu,
A1 - Rong Jin,
PY - 2006
KW - Content-based image retrieval
KW - relevance feedback
KW - log-based relevance feedback
KW - log data
KW - user issues
KW - semantic gap
KW - support vector machines.
VL - 18
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named Soft Label Support Vector Machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

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Index Terms:
Content-based image retrieval, relevance feedback, log-based relevance feedback, log data, user issues, semantic gap, support vector machines.
Citation:
Steven C.H. Hoi, Michael R. Lyu, Rong Jin, "A Unified Log-Based Relevance Feedback Scheme for Image Retrieval," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 4, pp. 509-524, April 2006, doi:10.1109/TKDE.2006.53
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