Human action recognition using genetic algorithms and convolutional neural networks

Ijjina, E P and C, Krishna Mohan (2016) Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognition, 59. pp. 199-212. ISSN 0031-3203

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In this paper, an approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed. We demonstrate that initializing the weights of a convolutional neural network (CNN) classifier based on solutions generated by genetic algorithms (GA) minimizes the classification error. A gradient descent algorithm is used to train the CNN classifiers (to find a local minimum) during fitness evaluations of GA chromosomes. The global search capabilities of genetic algorithms and the local search ability of gradient descent algorithm are exploited to find a solution that is closer to global-optimum. We show that combining the evidences of classifiers generated using genetic algorithms helps to improve the performance. We demonstrate the efficacy of the proposed classification system for human action recognition on UCF50 dataset.

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Item Type: Article
Uncontrolled Keywords: Convolutional Neural Network (CNN); Genetic algorithms (GA); Human action recognition; Action bank features
Subjects: Computer science > Special computer methods
Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 01 Feb 2016 09:49
Last Modified: 17 Oct 2017 10:01
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