TY - GEN
T1 - Subspace clustering and label propagation for active feedback in image retrieval
AU - Qin, Tao
AU - Liu, Tie-Yan
AU - Zhang, Xu-Dong
AU - Ma, Wei-Ying
AU - Zhang, Hong-Jiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwilling to provide many feedbacks, the insufficiency of the training samples limited the success of relevance feedback. To tackle this problem, we propose two coupled algorithms: (i) overlapped subspace clustering to select representative images for users feedback; and (ii) multi-subspace label propagation to include unlabeled data in the training process. As these two algorithms are both working on sub feature spaces of the image database, they can not only deal with the insufficient training samples but also well capture the users attention during the retrieval process. Experimental results on a large database of general-purposed images demonstrated the high effectiveness of our proposed algorithms. © 2005 IEEE.
AB - In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwilling to provide many feedbacks, the insufficiency of the training samples limited the success of relevance feedback. To tackle this problem, we propose two coupled algorithms: (i) overlapped subspace clustering to select representative images for users feedback; and (ii) multi-subspace label propagation to include unlabeled data in the training process. As these two algorithms are both working on sub feature spaces of the image database, they can not only deal with the insufficient training samples but also well capture the users attention during the retrieval process. Experimental results on a large database of general-purposed images demonstrated the high effectiveness of our proposed algorithms. © 2005 IEEE.
UR - https://www.scopus.com/pages/publications/84858377737
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84858377737&origin=recordpage
U2 - 10.1109/MMMC.2005.69
DO - 10.1109/MMMC.2005.69
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769521649
SN - 9780769521640
T3 - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
SP - 172
EP - 179
BT - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
T2 - 11th International Multimedia Modelling Conference, MMM 2005
Y2 - 12 January 2005 through 14 January 2005
ER -