TY - GEN
T1 - CNN vs. SIFT for image retrieval
T2 - 24th ACM Multimedia Conference, MM 2016
AU - Yan, Ke
AU - Wang, Yaowei
AU - Liang, Dawei
AU - Huang, Tiejun
AU - Tian, Yonghong
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scenelevel, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.
AB - In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scenelevel, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.
KW - CNN
KW - Complementary CNN and SIFT (CCS)
KW - Multi-level image representation
KW - SIFT
UR - http://www.scopus.com/inward/record.url?scp=84994663075&partnerID=8YFLogxK
U2 - 10.1145/2964284.2967252
DO - 10.1145/2964284.2967252
M3 - Conference contribution
AN - SCOPUS:84994663075
T3 - MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
SP - 407
EP - 411
BT - MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
Y2 - 15 October 2016 through 19 October 2016
ER -