Compacted Dither Pattern Codes versus Principal Component Analysis in video visual depiction

Requirement of reduction of feature space of visual descriptors gets attention due to negative effects of high dimensional feature space. This paper reports the performance of Compacted Dither Pattern Code (CDPC) over Principal Component Analysis (PCA) based compact colour descriptor. There are several competitive advantages of CDPC in feature extraction and classification stages when compared to PCA feature vectors. The embedded texel properties, spatial colour arrangements, high compactness, and robust feature representation of CDPC have proven its performances in our experimental study. Visual description experiments were conducted for ten irregular shapes based visual concepts in videos with three setups namely CDPC with Bhattacharyya classifier, PCA with Support Vector Machine (SVM) classifier and PCA with Bhattacharyya classifier. The experimental results were presented based on three common performance measures. The results depict that CDPC with Bhattacharyya classifier provides a good generalized performance for irregular shape based visual description as compared to the other two experimental setups.