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Proceedings Paper

Combining heterogeneous features for 3D hand-held object recognition
Author(s): Xiong Lv; Shuang Wang; Xiangyang Li; Shuqiang Jiang
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Paper Abstract

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Paper Details

Date Published: 29 October 2014
PDF: 10 pages
Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92732I (29 October 2014); doi: 10.1117/12.2071381
Show Author Affiliations
Xiong Lv, Institute of Computing Technology (China)
Shuang Wang, Institute of Computing Technology (China)
Xiangyang Li, Capital Normal Univ. (China)
Shuqiang Jiang, Institute of Computing Technology (China)


Published in SPIE Proceedings Vol. 9273:
Optoelectronic Imaging and Multimedia Technology III
Qionghai Dai; Tsutomu Shimura, Editor(s)

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