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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45157

Title: Moment-based symmetry detection for scene modeling and recognition using RGB-D images
Authors: Jui-Yuan Su
Shyi-Chyi Cheng
Jun-Wei Hsieh
Tzu-Hao Hsu
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: part-based scene modeling
moment-based symmetry detection
RGB-D images
symmetric patch detection
unsupervised feature representation
Date: 2016-12
Issue Date: 2018-01-30T08:00:13Z
Publisher: Pattern Recognition (ICPR), 2016 23rd International Conference on
Abstract: Abstract:
In this paper we present a novel unsupervised feature representation by extracting salient symmetries in RGB-D images using the proposed moment-based symmetric patch detector. A fast indexing structure is also derived to group local symmetric patches into semantically meaningful symmetric parts. Given an RGB-D image, the hash-based symmetric patch indexing speeds up the searches of symmetric patch pairs, which are further grouped into symmetric parts with nearly linear time complexity. In the context of symmetry matching and scene classification, the second part of this work presents a symmetry-based scene modeling, aiming at computing a robust part-based feature set for each image category. To verify the effectiveness of the symmetry detector, based on the pre-learned part-based scene model, a part-based voting scheme is constructed to annotate the scene type of the input RGB-D image. Experimental results show that the proposed approach outperforms the compared methods in terms of detection and recognition accuracy using publicly available datasets.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45157
Appears in Collections:[資訊工程學系] 演講及研討會

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