Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification

Liu, Li, Shao, Ling and Rockett, Peter (2013) Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification. Signal Processing, 93 (6). pp. 1521-1530. ISSN 0165-1684

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Abstract

In this paper we propose a method of feature selection using the AdaBoost algorithm for action recognition. Instead of detecting spatio-temporal interest points and using a ‘bag of features’ approach, we use densely sampled descriptors, either 3D-SIFT or 3D-HOG, and select the most discriminative subset using the AdaBoost algorithm. We obtain maximal accuracy with just 200 of the 3217 possible raw 3D features from each video sequence. Using the extremely simple naive Bayes nearest-neighbor (NBNN) classifier with the most discriminative 3D-SIFT features, we obtain accuracies of: 92.7%, 99.4%, 92.3% and 38.1% on the KTH, Weizmann, IXMAS and HMDB51 datasets, respectively. We also observe that the errors are reasonably equitably distributed across the different action classes.

Item Type:	Article
Uncontrolled Keywords:	Feature selection; AdaBoost; Naive Bayes nearest-neighbor classifier
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Publisher
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 14:44
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22839

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