Sparse F-IncSFA for action recognition

Loo, C. and Bardia, Y. (2012) Sparse F-IncSFA for action recognition. In: JSME Conference on Robotics and Mechatronics, 27-29 May 2012, Hamamatsu, Japan.

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High dimensional input streams and unsupervised learning are two important factors in the area of humanoids and processes of the actions and movements of human. Our Fast Incremental Slow Feature Analysis (F-IncSFA) can learn and extract the few significant features of the complex sensory input sequences regarding high-level spatio-temporal conceptions. In this paper, the application of the F-IncSFA and some of its structure to make a hierarchical compound network made of F-IncSFA has been described. Also the techniques developed by adding efficient sparse coding as an encoder and a preprocessing step before an application of the F-IncSFA. The efficient sparse coding can dramatically reduces the dimension of extracted features and outcome of the efficient sparse coding are quite small as compared with the size of high-dimension video obtained by humanoid or human action. It has revealed excellent and promising dimension reduction by this preprocessor.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sparse fast incremental slow feature analysis (Sparse-F-IncSFA), unsupervised learning, hierarchical network, efficient sparse coding.
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Mr. Mohd Samsul Ismail
Date Deposited: 22 Sep 2015 00:05
Last Modified: 22 Sep 2015 00:05

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