NDLI: Riemannian manifold-based support vector machine for human activity classification in images

Content Provider	IEEE Xplore Digital Library
Author	Yixiao Yun Gu, I.Y.-H. Aghajan, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Signals & Syst., Chalmers Univ. of Technol., Goteborg, Sweden (Yixiao Yun; Gu, I.Y.-H.) \|\| Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA (Aghajan, H.)
Abstract	This paper addresses the issue of classification of human activities in still images. We propose a novel method where part-based features focusing on human and object interaction are utilized for activity representation, and classification is designed on manifolds by exploiting underlying Riemannian geometry. The main contributions of the paper include: (a) represent human activity by appearance features from image patches containing hands, and by structural features formed from the distances between the torso and patch centers; (b) formulate SVM kernel function based on the geodesics on Riemannian manifolds under the log-Euclidean metric; (c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 2750 images in 7 classes of activities from 10 subjects. Results have shown good performance (average classification rate of 95.83%, false positive rate of 0.71%). Comparisons with three other related classifiers provide further support to the proposed method.
Starting Page	3466
Ending Page	3469
File Size	925529
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machine (SVM) Human activity classification Riemannian manifold covariance descriptor symmetric positive definite matrices
Content Type	Text
Resource Type	Article

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