TY - GEN
T1 - Fusing pointwise and pairwise labels for supporting user-adaptive image retrieval
AU - Chen, Lin
AU - Zhang, Peng
AU - Li, Baoxin
N1 - Funding Information:
The work was supported in part by a grant from National Science Foundation and a grant from Army Research Office. Any opinions and conclusions expressed in this material are those of the author(s) and do not necessarily reect the view of sponsors.
PY - 2015/6/22
Y1 - 2015/6/22
N2 - User-adaptive image retrieval/recommendation has drawn a lot of research interests in recent years, owing to fast development of various Web applications where retrieving images is a key enabling task. Existing challenges include the lack of user-adaptive training data, the ambiguity of user query and the real-time interactivity of a system. This paper proposes a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. Under this framework, we develop an online learning algorithm for updating the ranking performance based on user feedback. Furthermore, we derive the framework into a kernel form, allowing easy application of kernel techniques. The proposed approach is evaluated on two image datasets and experimental results show that it achieves obvious performance gains over ranking and zero-shot learning from either type of training data independently. In addition, the online learning algorithm is able to deliver much better performance than batch learning, given the same elapsed running time, or can achieve better performance in much less time.
AB - User-adaptive image retrieval/recommendation has drawn a lot of research interests in recent years, owing to fast development of various Web applications where retrieving images is a key enabling task. Existing challenges include the lack of user-adaptive training data, the ambiguity of user query and the real-time interactivity of a system. This paper proposes a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. Under this framework, we develop an online learning algorithm for updating the ranking performance based on user feedback. Furthermore, we derive the framework into a kernel form, allowing easy application of kernel techniques. The proposed approach is evaluated on two image datasets and experimental results show that it achieves obvious performance gains over ranking and zero-shot learning from either type of training data independently. In addition, the online learning algorithm is able to deliver much better performance than batch learning, given the same elapsed running time, or can achieve better performance in much less time.
KW - Adaptive image retrieval
KW - Attribute learning
KW - Learning to rank
UR - http://www.scopus.com/inward/record.url?scp=84962376481&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962376481&partnerID=8YFLogxK
U2 - 10.1145/2671188.2749358
DO - 10.1145/2671188.2749358
M3 - Conference contribution
AN - SCOPUS:84962376481
T3 - ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval
SP - 67
EP - 74
BT - ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval
PB - Association for Computing Machinery, Inc
T2 - 5th ACM International Conference on Multimedia Retrieval, ICMR 2015
Y2 - 23 June 2015 through 26 June 2015
ER -