Computational Intelligence and Multimedia Applications, International Conference on (2005)
Las Vegas, Nevada
Aug. 16, 2005 to Aug. 18, 2005
S. Arivazhagan , Mepco Schlenk Enggineering College
L. Ganesan , A.C. College of Enggineering and Technology
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.
Texture, Wavelet, MRMRF Feature, Wavelet Statistical Feature, Wavelet Cooccurrence Feature, Feature extraction and Texture classification
S. Arivazhagan, L. Ganesan, "Performance Analysis of Texture Classification Techniques Using MRMRF and WSFS & WCFS", Computational Intelligence and Multimedia Applications, International Conference on, vol. 00, no. , pp. 297-302, 2005, doi:10.1109/ICCIMA.2005.46