NDLI: Learning hierarchical representation with sparsity for RGB-D object recognition

Content Provider	IEEE Xplore Digital Library
Author	Kuan-Ting Yu Shih-Huan Tseng Li-Chen Fu
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Information Engineering and Department of Electrical Engineering, National Taiwan University, Taiwan (Li-Chen Fu) \|\| Department of Computer Science and Information Engineering, National Taiwan University, Taiwan (Kuan-Ting Yu; Shih-Huan Tseng)
Abstract	RGB-D sensor has gained its popularity in the study of object recognition for its low cost as well as its capability to provide synchronized RGB and depth images. Thus, researchers have proposed new methods to extract features from RGB-D data. On the other hand, learning-based feature representation is a promising approach for 2D image classification. By exploiting sparsity in 2D image signals, we can learn image representation instead of using hand-crafted local descriptors like SIFT or HoG. This framework inspired us to learn features from RGB-D data. Our work focuses on two goals. First, we propose a novel Hierarchical Sparse Shape Descriptor (HSSD) to form learning-based representation for 3D shapes. To achieve this, we analyze several 3D feature extraction techniques and propose a unified view of them. Then, we learn hierarchical shape representation with sparse coding, max pooling and local grouping. Second, we investigate whether RGB and depth information should be fused at lower level or higher level. Experimental results show that, first, our HSSD algorithm can learn shape dictionary and provide shape cues in addition to the 2D cues. Using the proposed HSSD algorithm achieves 84% accuracy on a household RGB-D object dataset and outperforms a widely used VFH shape feature by 13%. Second, fusing RGB-D information at lower level does not improve recognition performance.
Starting Page	3011
Ending Page	3016
File Size	888052
Page Count	6
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6386175
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Filter banks Feature extraction Encoding Dictionaries Accuracy Histograms
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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