TY - GEN
T1 - Object detection oriented feature pooling for video semantic indexing
AU - Ueki, Kazuya
AU - Kobayashi, Tetsunori
N1 - Funding Information:
This work was partially supported by JSPS KAK-ENHI Grant Number 15K00249 and Waseda University Grant for Special Research Projects 2016A-026.
PY - 2017
Y1 - 2017
N2 - We propose a new feature extraction method for video semantic indexing. Conventional methods extract features densely and uniformly across an entire image, whereas the proposed method exploits the object detector to extract features from image windows with high objectness. This feature extraction method focuses on "objects." Therefore, we can eliminate the unnecessary background information, and keep the useful information such as the position, the size, and the aspect ratio of a object. Since these object detection oriented features are complementary to features from entire images, the performance of video semantic indexing can be further improved. Experimental comparisons using large-scale video dataset of the TRECVID benchmark demonstrated that the proposed method substantially improved the performance of video semantic indexing.
AB - We propose a new feature extraction method for video semantic indexing. Conventional methods extract features densely and uniformly across an entire image, whereas the proposed method exploits the object detector to extract features from image windows with high objectness. This feature extraction method focuses on "objects." Therefore, we can eliminate the unnecessary background information, and keep the useful information such as the position, the size, and the aspect ratio of a object. Since these object detection oriented features are complementary to features from entire images, the performance of video semantic indexing can be further improved. Experimental comparisons using large-scale video dataset of the TRECVID benchmark demonstrated that the proposed method substantially improved the performance of video semantic indexing.
KW - Convolutional neural network
KW - Object detection
KW - Video retrieval
KW - Video semantic indexing
UR - http://www.scopus.com/inward/record.url?scp=85047848447&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047848447&partnerID=8YFLogxK
U2 - 10.5220/0006099600440051
DO - 10.5220/0006099600440051
M3 - Conference contribution
AN - SCOPUS:85047848447
T3 - VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 44
EP - 51
BT - VISAPP
A2 - Imai, Francisco
A2 - Tremeau, Alain
A2 - Braz, Jose
PB - SciTePress
T2 - 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Y2 - 27 February 2017 through 1 March 2017
ER -