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Title: Object proposal via depth connectivity constrained grouping
Authors: Wang, Y
Huang, L
Ren, T
Zhong, SH
Liu, Y 
Wu, G
Keywords: Constrained grouping
Depth connectivity
Object proposal
RGB-D image
Issue Date: 2018
Publisher: Springer Verlag
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, v. 10736 LNCS, p. 34-44 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Object proposal aims to detect category-independent object candidates with a limited number of bounding boxes. In this paper, we propose a novel object proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth aware hierarchical segmentation. Next, we combine the segments into hypotheses hierarchically on each level, and further expand these hypotheses to object candidates using depth connectivity constrained region growing. Finally, we score the object candidates based on their color and depth features, and select the ones with the highest scores as the object proposal result. We validated the proposed method on the largest RGB-D image data set for object proposal, and our method is superior to the state-of-the-art methods.
Description: 18th Pacific-Rim Conference on Multimedia, PCM 2017, Harbin, China, 28-29 September 2017
ISBN: 9783319773827
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-77383-4_4
Appears in Collections:Conference Paper

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