Sparse F-IncSFA for action recognition
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 seque...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/14089/1/1A1-P04.pdf |
| _version_ | 1825720468015939584 |
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| author | Loo, C. Bardia, Y. |
| author_facet | Loo, C. Bardia, Y. |
| author_sort | Loo, C. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:35:36Z |
| format | Conference or Workshop Item |
| id | um.eprints-14089 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:35:36Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | um.eprints-140892015-09-22T00:05:20Z http://eprints.um.edu.my/14089/ Sparse F-IncSFA for action recognition Loo, C. Bardia, Y. T Technology (General) 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. 2012-05 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/14089/1/1A1-P04.pdf 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. |
| spellingShingle | T Technology (General) Loo, C. Bardia, Y. Sparse F-IncSFA for action recognition |
| title | Sparse F-IncSFA for action recognition |
| title_full | Sparse F-IncSFA for action recognition |
| title_fullStr | Sparse F-IncSFA for action recognition |
| title_full_unstemmed | Sparse F-IncSFA for action recognition |
| title_short | Sparse F-IncSFA for action recognition |
| title_sort | sparse f incsfa for action recognition |
| topic | T Technology (General) |
| url | http://eprints.um.edu.my/14089/1/1A1-P04.pdf |
| work_keys_str_mv | AT looc sparsefincsfaforactionrecognition AT bardiay sparsefincsfaforactionrecognition |