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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2nd International Conference on Digital Image Processing
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Conference on Digital Image Processing - Singapore, Singapore
Duration: 26 Feb 201028 Feb 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7546

Conference

Conference2nd International Conference on Digital Image Processing
Country/TerritorySingapore
CitySingapore
Period26/02/201028/02/2010

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