Statistical tools for evaluating classification efficacy of feature extraction techniques

Debdoot Sheet; Vikram Venkatraghavan; Amit Suveer; Hrushikesh Garud; Jyotirmoy Chatterjee; Manjunatha Mahadevappa; Ajoy K. Ray

doi:10.1117/12.853168

26 February 2010 Statistical tools for evaluating classification efficacy of feature extraction techniques

Debdoot Sheet, Vikram Venkatraghavan, Amit Suveer, Hrushikesh Garud, Jyotirmoy Chatterjee, Manjunatha Mahadevappa, Ajoy K. Ray

Author Affiliations +

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75461B (2010) https://doi.org/10.1117/12.853168
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

Abstract

Feature extraction using linguistic abstracts described by field experts, and their pragmatic behavior when tested with an inference engine is of interest to computer vision researchers. Advances in image processing have added to the complexity involved with selecting an appropriate feature extraction method for describing a linguistic feature. In this work, we propose the usage of a set of statistical tools for evaluating the efficacy of a feature extraction technique suitable for expressing a linguistic feature. This set of tools are based on expression of class discrimination strength of features, overlap in their expression, and the density of outliers present in them. The feature extraction techniques are ranked based on the scores obtained by them when tested with these tools. An experimental study for validating these claims, based on classification of two different visual texture, expressed using six different texture quantification techniques is also presented.

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Debdoot Sheet, Vikram Venkatraghavan, Amit Suveer, Hrushikesh Garud, Jyotirmoy Chatterjee, Manjunatha Mahadevappa, and Ajoy K. Ray "Statistical tools for evaluating classification efficacy of feature extraction techniques", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75461B (26 February 2010); https://doi.org/10.1117/12.853168

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