Commensurate dimensionality reduction for extended local ternary patterns

We present a systematic approach to reduce the dimensionality of the feature vector for local binary/ternary patterns. The proposed framework examines the distribution of uniform patterns in different image sets to formulate a procedure to assign dimensionality to uniform and non-uniform patterns. Unlike previous methods where all the information from non-uniform patterns is discarded or merged into a single dimension, the proposed commensurate dimensionality reduction (CDR) technique attempts to retain valuable information from all contributory factors. Experiments and comparative analysis have validated the efficacy of the newly defined CDR-ELTP descriptor in terms of noise resistance and texture classification.