Title

Video Object Co-Segmentation By Regulated Maximum Weight Cliques

Keywords

Cosegmentation; Video Segmentation

Abstract

In this paper, we propose a novel approach for object co-segmentation in arbitrary videos by sampling, tracking and matching object proposals via a Regulated Maximum Weight Clique (RMWC) extraction scheme. The proposed approach is able to achieve good segmentation results by pruning away noisy segments in each video through selection of object proposal tracklets that are spatially salient and temporally consistent, and by iteratively extracting weighted groupings of objects with similar shape and appearance (with-in and across videos). The object regions obtained from the video sets are used to initialize per-pixel segmentation to get the final co-segmentation results. Our approach is general in the sense that it can handle multiple objects, temporary occlusions, and objects going in and out of view. Additionally, it makes no prior assumption on the commonality of objects in the video collection. The proposed method is evaluated on publicly available multi-class video object co-segmentation dataset and demonstrates improved performance compared to the state-of-the-art methods. © 2014 Springer International Publishing.

Publication Date

1-1-2014

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

8695 LNCS

Issue

PART 7

Number of Pages

551-566

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-10584-0_36

Socpus ID

84906347042 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84906347042

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