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Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05)
Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS
Las Vegas, Nevada
August 16-August 18
ISBN: 0-7695-2358-7
| ASCII Text | x | ||
| S. Arivazhagan, L. Ganesan, "Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS," Computational Intelligence and Multimedia Applications, International Conference on, pp. 297-302, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005. | |||
| BibTex | x | ||
| @article{ 10.1109/ICCIMA.2005.46, author = {S. Arivazhagan and L. Ganesan}, title = {Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS}, journal ={Computational Intelligence and Multimedia Applications, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2358-7}, pages = {297-302}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICCIMA.2005.46}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Computational Intelligence and Multimedia Applications, International Conference on TI - Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS SN - 0-7695-2358-7 SP297 EP302 A1 - S. Arivazhagan, A1 - L. Ganesan, PY - 2005 KW - Texture KW - Wavelet KW - MRMRF Feature KW - Wavelet Statistical Feature KW - Wavelet Cooccurrence Feature KW - Feature extraction and Texture classification VL - 0 JA - Computational Intelligence and Multimedia Applications, International Conference on ER - | |||
Texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. This paper analyses the performance of texture classification techniques using (i) Multi Resolution Markov Random Field (MRMRF) features and (ii) a combination of Wavelet Statistical Features (WSFs) and Wavelet Co-occurrence Features (WCFs) with two different texture datasets.
Index Terms:
Texture, Wavelet, MRMRF Feature, Wavelet Statistical Feature, Wavelet Cooccurrence Feature, Feature extraction and Texture classification
Citation:
S. Arivazhagan, L. Ganesan, "Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS," iccima, pp.297-302, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005
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