Abstract
Feature Extraction is the method of capturing the visual content of images for indexing and retrieval. It simplifies the amount of information required to describe a large set of data. In computer vision, feature detection refers to the computation of local image features from the image. Texture is the core element in numerous computer vision applications Orthogonal Polynomial Operators are generated from a basis operator of fixed size and their efficiency in extracting texture features is studied. These operators act as filters and their responses on images are considered as feature space. From each filtered image statistical features are extracted and an optimal operator set is designed by incorporating a feature selection approach. Mahalanobis separability metric is used in the feature selection process. The optimal operator set removes insignificant operators and thus improves the performance of texture classification. Experimental results on benchmark datasets prove the effectiveness of the proposed approach.
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Suguna, R., Anandhakumar, P. (2011). Finding Optimal Set of Orthogonal Polynomial Operators for Efficient Texture Feature Extraction. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_9
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DOI: https://doi.org/10.1007/978-3-642-24055-3_9
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