NDLI: Semantic object segmentation via detection in weakly labeled video

Content Provider	IEEE Xplore Digital Library
Author	Yu Zhang Xiaowu Chen Jia Li Chen Wang Changqun Xia
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China (Yu Zhang; Xiaowu Chen; Jia Li; Chen Wang; Changqun Xia)
Abstract	Semantic object segmentation in video is an important step for large-scale multimedia analysis. In many cases, however, semantic objects are only tagged at video-level, making them difficult to be located and segmented. To address this problem, this paper proposes an approach to segment semantic objects in weakly labeled video via object detection. In our approach, a novel video segmentation-by-detection framework is proposed, which first incorporates object and region detectors pre-trained on still images to generate a set of detection and segmentation proposals. Based on the noisy proposals, several object tracks are then initialized by solving a joint binary optimization problem with min-cost flow. As such tracks actually provide rough configurations of semantic objects, we thus refine the object segmentation while preserving the spatiotemporal consistency by inferring the shape likelihoods of pixels from the statistical information of tracks. Experimental results on Youtube-Objects dataset and SegTrack v2 dataset demonstrate that our method outperforms state-of-the-arts and shows impressive results.
Starting Page	3641
Ending Page	3649
File Size	1532736
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298987
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proposals Semantics Shape Image segmentation Noise measurement Object segmentation Detectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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