A novel information theoretic approach to wavelet feature selection for texture classification
In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/2810 |
| _version_ | 1848744055223091200 |
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| author | Naseem, Imran Pham, DucSon Venkatesh, S |
| author_facet | Naseem, Imran Pham, DucSon Venkatesh, S |
| author_sort | Naseem, Imran |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (EI) criterion. Only the subbands with the top EI criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion. |
| first_indexed | 2025-11-14T05:55:23Z |
| format | Journal Article |
| id | curtin-20.500.11937-2810 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:55:23Z |
| publishDate | 2012 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-28102017-09-13T16:02:17Z A novel information theoretic approach to wavelet feature selection for texture classification Naseem, Imran Pham, DucSon Venkatesh, S mutual information texture classification effective information In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (EI) criterion. Only the subbands with the top EI criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion. 2012 Journal Article http://hdl.handle.net/20.500.11937/2810 10.1016/j.compeleceng.2012.11.003 Elsevier restricted |
| spellingShingle | mutual information texture classification effective information Naseem, Imran Pham, DucSon Venkatesh, S A novel information theoretic approach to wavelet feature selection for texture classification |
| title | A novel information theoretic approach to wavelet feature selection for texture classification |
| title_full | A novel information theoretic approach to wavelet feature selection for texture classification |
| title_fullStr | A novel information theoretic approach to wavelet feature selection for texture classification |
| title_full_unstemmed | A novel information theoretic approach to wavelet feature selection for texture classification |
| title_short | A novel information theoretic approach to wavelet feature selection for texture classification |
| title_sort | novel information theoretic approach to wavelet feature selection for texture classification |
| topic | mutual information texture classification effective information |
| url | http://hdl.handle.net/20.500.11937/2810 |