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

• Published in: 2013 IEEE International Conference on Image Processing pp. 3466 - 3469
• Main Authors: Yixiao Yun, Gu, Irene Yu-Hua, Aghajan, Hamid
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-01-2013
• Subjects: covariance descriptor; Human activity classification; Riemannian manifold; support vector machine (SVM); symmetric positive definite matrices
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2013.6738715

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.