TY - GEN
T1 - A lazy processing approach to user relevance feedback for content-based image retrieval
AU - Nilpanich, Sirikunya
AU - Hua, Kien A.
AU - Petkova, Antoniya
AU - Ho, Yao H.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. However, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback; (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples; and (3) The high feature dimensions compared to the size of the training set causes over fitting. We propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of local classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach.
AB - User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. However, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback; (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples; and (3) The high feature dimensions compared to the size of the training set causes over fitting. We propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of local classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach.
KW - Content-based image retrieval
KW - Machine learning
KW - Relevance feedback
UR - http://www.scopus.com/inward/record.url?scp=79951755954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951755954&partnerID=8YFLogxK
U2 - 10.1109/ISM.2010.58
DO - 10.1109/ISM.2010.58
M3 - Conference contribution
AN - SCOPUS:79951755954
SN - 9780769542171
T3 - Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
SP - 342
EP - 346
BT - Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
T2 - 2010 IEEE International Symposium on Multimedia, ISM 2010
Y2 - 13 December 2010 through 15 December 2010
ER -