Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet
In this paper, a new computation for gray level co-occurrence matrix (GLCM) is proposed. The aim is to reduce the computation burden of the original GLCM computation. The proposed computation will be based on Haar wavelet transform. Haar wavelet transform is chosen because the resulting wavelet band...
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| Format: | Article |
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2007
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| Online Access: | http://eprints.utm.my/8774/ http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.45 |
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| _version_ | 1845472076051251200 |
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| author | Abu Bakar, S.A.R Mokji, M.M |
| author_facet | Abu Bakar, S.A.R Mokji, M.M |
| author_sort | Abu Bakar, S.A.R |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In this paper, a new computation for gray level co-occurrence matrix (GLCM) is proposed. The aim is to reduce the computation burden of the original GLCM computation. The proposed computation will be based on Haar wavelet transform. Haar wavelet transform is chosen because the resulting wavelet bands are strongly correlated with the orientation elements in the GLCM computation. The second reason is because the total pixel entries for Haar wavelet transform is always minimum. Thus, the GLCM computation burden can be reduced. The proposed computation is tested with the classification performance of the Brodatz texture images. Although the aim is to achieve at least similar performance with the original GLCM computation, the proposed computation gives a slightly better performance compare to the original GLCM computation.
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| format | Article |
| id | my.utm.eprints-8774 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2007 |
| record_format | eprints |
| spelling | my.utm.eprints-87742017-10-19T03:43:44Z http://eprints.utm.my/8774/ Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet Abu Bakar, S.A.R Mokji, M.M In this paper, a new computation for gray level co-occurrence matrix (GLCM) is proposed. The aim is to reduce the computation burden of the original GLCM computation. The proposed computation will be based on Haar wavelet transform. Haar wavelet transform is chosen because the resulting wavelet bands are strongly correlated with the orientation elements in the GLCM computation. The second reason is because the total pixel entries for Haar wavelet transform is always minimum. Thus, the GLCM computation burden can be reduced. The proposed computation is tested with the classification performance of the Brodatz texture images. Although the aim is to achieve at least similar performance with the original GLCM computation, the proposed computation gives a slightly better performance compare to the original GLCM computation. 2007 Article PeerReviewed Abu Bakar, S.A.R and Mokji, M.M (2007) Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet. Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet, N/A (n/a). pp. 273-279. ISSN 0-7695-2928-3 http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.45 10.1109/CGIV.2007.45 |
| spellingShingle | Abu Bakar, S.A.R Mokji, M.M Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title | Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title_full | Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title_fullStr | Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title_full_unstemmed | Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title_short | Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet |
| title_sort | gray level co-occurrence matrix computation based on haar wavelet |
| url | http://eprints.utm.my/8774/ http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.45 |
| url_provider | http://eprints.utm.my/ |