NDLI: Weakly Supervised Multiclass Video Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Xiao Liu Dacheng Tao Mingli Song Ying Ruan Chun Chen Jiajun Bu
Copyright Year	2014
Description	Author affiliation: Zhejiang Provincial Key Lab. of Service Robot, Zhejiang Univ., Hangzhou, China (Xiao Liu; Mingli Song; Ying Ruan; Chun Chen; Jiajun Bu) \|\| Centre for Quantum Comput. & Intell. Syst., Univ. of Technol., Sydney, NSW, Australia (Dacheng Tao)
Abstract	The desire of enabling computers to learn semantic concepts from large quantities of Internet videos has motivated increasing interests on semantic video understanding, while video segmentation is important yet challenging for understanding videos. The main difficulty of video segmentation arises from the burden of labeling training samples, making the problem largely unsolved. In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation. Whereas previous weakly supervised video segmentation methods have been limited to the two-class case, our proposed scheme focuses on more challenging multiclass video segmentation, which finds a semantically meaningful label for every pixel in a video. Our scheme enjoys several favorable properties when compared with conventional methods. First, a weakly supervised hashing procedure is carried out to handle both metric and semantic similarity. Second, the proposed nearest neighbor-based label transfer algorithm effectively avoids overfitting caused by weakly supervised data. Third, a multi-video graph model is built to encourage smoothness between regions that are spatiotemporally adjacent and similar in appearance. We demonstrate the effectiveness of the proposed scheme by comparing it with several other state-of-the-art weakly supervised segmentation methods on one new Wild8 dataset and two other publicly available datasets.
Starting Page	57
Ending Page	64
File Size	1745582
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Streaming media Labeling Semantics Image segmentation Training Measurement Spatiotemporal phenomena label transfer video segmentation weakly supervised learning nearest neighbor
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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