TY - GEN
T1 - Statistical tools for evaluating classification efficacy of feature extraction techniques
AU - Sheet, Debdoot
AU - Venkatraghavan, Vikram
AU - Suveer, Amit
AU - Garud, Hrushikesh
AU - Chatterjee, Jyotirmoy
AU - Mahadevappa, Manjunatha
AU - Ray, Ajoy K.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77949520640&partnerID=8YFLogxK
U2 - https://doi.org/10.1117/12.853168
DO - https://doi.org/10.1117/12.853168
M3 - Conference contribution
SN - 9780819479426
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 2nd International Conference on Digital Image Processing
T2 - 2nd International Conference on Digital Image Processing
Y2 - 26 February 2010 through 28 February 2010
ER -