NDLI: Effective constructing training sets for object detection

Content Provider	IEEE Xplore Digital Library
Author	Weining Wu Yang Liu Wei Zeng Maozu Guo Chunyu Wang Xiaoyan Liu
Copyright Year	2013
Description	Author affiliation: NEC Labs., China (Wei Zeng) \|\| Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China (Weining Wu; Yang Liu; Maozu Guo; Chunyu Wang; Xiaoyan Liu)
Abstract	This paper addresses the problem of building up effective training sets at minimal labeling cost for object detection. This problem occurs in the situation that the part-based detector is trained on a group of positive examples with bounding box labels, but the images selected by uniform sampling do not reflect the desired training distribution and need additional labeling cost in order to obtain enough positive examples. We study the active training process in which some object windows are sampled from a pool of unlabeled candidate windows, and then their corresponding bounding annotations are queried. We derive an effective training set by selecting a group of most uncertain object windows according to the current detector. Our approach has been empirically demonstrated on the object detection task of PASCAL VOC dataset. The experiment results show that our proposed algorithm outperforms common uniform sampling within the same labeling cost.
Starting Page	3377
Ending Page	3380
File Size	1111827
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738696
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	labeling cost object detection active learning sampling strategy
Content Type	Text
Resource Type	Article

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