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Articles by J.P. Ananth
Total Records ( 2 ) for J.P. Ananth
  M.A. Leo Vijilious , J.P. Ananth and V. Subbiah Bharathi
  This study presents a statistical approach to texture classification from a single image obtained under unknownviewpoint and illumination. Unlike in prior work, in which texture primitives (textons) are defined in a filter-responsespace and texture classes modeled by frequency histograms of these textons, we seek to extract and model geometric and photometric properties of image regions defining the texture. To this end, texture images are first segmented bya multiscale segmentation algorithm and a universal set of texture primitives is specified over all texture classes in the domain of region geometric and photometric properties. Then, for each class, a Tree-Structured Belief Network (TSBN) is learned, where nodes represent the corresponding image regions and edges, their statistical dependecies. A given unknown texture is classified with respect to themaximum posterior distribution of the TSBN. Experimental results on the benchmark CUReT database demonstrate that our approach outperforms the state-of-the-artmethods.
  J.P. Ananth , M.A. Leo Vijilous and V. Subbiah Bharathi
  In this study feature extraction process is analyzed and a new set of edge features is proposed. A revised edge-based structural feature extraction approach is introduced. A principle feature selection algorithm is also proposed for new feature analysis and feature selection. The results of the PFA is tested and compared to the original feature set, random selections, as well as those from Principle Component Analysis and multivariate linear discriminant analysis. The experiments showed that the proposed features perform better than wavelet moment for image retrieval in a real world image database and the feature selected by the proposed algorithm yields comparable results to original feature setstudy better results than random sets.
 
 
 
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