TY - JOUR
T1 - User-adaptive image retrieval via fusing pointwise and pairwise labels
AU - Chen, Lin
AU - Zhang, Peng
AU - Li, Baoxin
N1 - Funding Information:
The work was supported in part by a grant from the Army Research Office (ARO). Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of the ARO.
Publisher Copyright:
© 2015, Springer-Verlag London.
PY - 2016/3/1
Y1 - 2016/3/1
N2 - In many Web image retrieval applications, adapting the retrieval results according to some model of the user is a desired feature as the returned images can be made specifically relevant to a user’s needs. Making retrieval user-adaptive faces several practical challenges, including the ambiguity of user query, the lack of user-adaptive training data, and lack of proper mechanisms for supporting adaptive learning. To address some of these challenges, we propose a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. An online learning algorithm is developed for updating the ranking performance based on user feedback. The framework is also derived into a kernel form allowing easy application of kernel techniques. We use both synthetic and real-world datasets to evaluate the performance of the proposed algorithm. Comparison with other state-of-the-art approaches suggests that our method achieves obvious performance gains over ranking and zero-shot learning. Further, our online learning algorithm was found to be able to deliver much better performance than batch learning, given the same elapsed running time.
AB - In many Web image retrieval applications, adapting the retrieval results according to some model of the user is a desired feature as the returned images can be made specifically relevant to a user’s needs. Making retrieval user-adaptive faces several practical challenges, including the ambiguity of user query, the lack of user-adaptive training data, and lack of proper mechanisms for supporting adaptive learning. To address some of these challenges, we propose a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. An online learning algorithm is developed for updating the ranking performance based on user feedback. The framework is also derived into a kernel form allowing easy application of kernel techniques. We use both synthetic and real-world datasets to evaluate the performance of the proposed algorithm. Comparison with other state-of-the-art approaches suggests that our method achieves obvious performance gains over ranking and zero-shot learning. Further, our online learning algorithm was found to be able to deliver much better performance than batch learning, given the same elapsed running time.
KW - Adaptive image retrieval
KW - Attribute learning
KW - Learning to rank
UR - http://www.scopus.com/inward/record.url?scp=85013903732&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013903732&partnerID=8YFLogxK
U2 - 10.1007/s13735-015-0092-1
DO - 10.1007/s13735-015-0092-1
M3 - Article
AN - SCOPUS:85013903732
SN - 2192-6611
VL - 5
SP - 19
EP - 33
JO - International Journal of Multimedia Information Retrieval
JF - International Journal of Multimedia Information Retrieval
IS - 1
ER -