TY - GEN
T1 - Object detection in 20 questions
AU - Chen, Xi Stephen
AU - He, He
AU - Davis, Larry S.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/23
Y1 - 2016/5/23
N2 - We propose a novel general strategy for object detection. Instead of passively evaluating all object detectors at all possible locations in an image, we develop a divide-and-conquer approach by actively and sequentially evaluating contextual cues related to the query based on the scene and previous evaluations - like playing a "20 Questions" game - to decide where to search for the object. We formulate the problem as a Markov Decision Process and learn a search policy by reinforcement learning. To demonstrate the efficacy of our generic algorithm, we apply the 20 questions approach in the recent framework of simultaneous object detection and segmentation. Experimental results on the Pascal VOC dataset show that our algorithm reduces about 45.3% of the object proposals and 36% of average evaluation time while achieving better average precision compared to exhaustive search.
AB - We propose a novel general strategy for object detection. Instead of passively evaluating all object detectors at all possible locations in an image, we develop a divide-and-conquer approach by actively and sequentially evaluating contextual cues related to the query based on the scene and previous evaluations - like playing a "20 Questions" game - to decide where to search for the object. We formulate the problem as a Markov Decision Process and learn a search policy by reinforcement learning. To demonstrate the efficacy of our generic algorithm, we apply the 20 questions approach in the recent framework of simultaneous object detection and segmentation. Experimental results on the Pascal VOC dataset show that our algorithm reduces about 45.3% of the object proposals and 36% of average evaluation time while achieving better average precision compared to exhaustive search.
UR - http://www.scopus.com/inward/record.url?scp=84977640189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977640189&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477562
DO - 10.1109/WACV.2016.7477562
M3 - Conference contribution
AN - SCOPUS:84977640189
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -