TY - GEN
T1 - Query expansion with pairwise learning in object retrieval challenge
AU - Liu, Hao
AU - Shimada, Atsushi
AU - Xu, Xing
AU - Nagahara, Hajime
AU - Uchiyama, Hideaki
AU - Taniguchi, Rin Ichiro
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/5/7
Y1 - 2015/5/7
N2 - Making a reasonable ranking on images in dataset is one of the main objectives for object retrieval challenge, and in this paper we intend to improve the ranking quality. We follow the idea of query expansion in previous researches. Based on the use of bag-of-visual-words model, tf-idf scoring and spatial verification, previous method applied a pointwise style learning in query expansion stage, using but not fully exploring verification results. We intend to extend their learning approach for better discriminative power in retrieval. In re-ranking stage we propose a method using pairwise learning, instead of pointwise learning previously used. We could obtain more reliable ranking on a shortlist of examples. If this verification itself is reliable, a good re-ranking should best preserve this sub-ranking order. Thus in our proposed method, we are motivated to leverage a pairwise learning method to incorporate the ranking sequential information more efficiently. We evaluate and compare our proposed method with previous methods over Oxford 5k dataset, a standard benchmark dataset, where our method achieve better mean average precision and showed better discriminative power.
AB - Making a reasonable ranking on images in dataset is one of the main objectives for object retrieval challenge, and in this paper we intend to improve the ranking quality. We follow the idea of query expansion in previous researches. Based on the use of bag-of-visual-words model, tf-idf scoring and spatial verification, previous method applied a pointwise style learning in query expansion stage, using but not fully exploring verification results. We intend to extend their learning approach for better discriminative power in retrieval. In re-ranking stage we propose a method using pairwise learning, instead of pointwise learning previously used. We could obtain more reliable ranking on a shortlist of examples. If this verification itself is reliable, a good re-ranking should best preserve this sub-ranking order. Thus in our proposed method, we are motivated to leverage a pairwise learning method to incorporate the ranking sequential information more efficiently. We evaluate and compare our proposed method with previous methods over Oxford 5k dataset, a standard benchmark dataset, where our method achieve better mean average precision and showed better discriminative power.
UR - http://www.scopus.com/inward/record.url?scp=84937154711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937154711&partnerID=8YFLogxK
U2 - 10.1109/FCV.2015.7103703
DO - 10.1109/FCV.2015.7103703
M3 - Conference contribution
AN - SCOPUS:84937154711
T3 - 2015 Frontiers of Computer Vision, FCV 2015
BT - 2015 Frontiers of Computer Vision, FCV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015
Y2 - 28 January 2015 through 30 January 2015
ER -