Stationary Features and Cat Detection

Abstract

Most discriminative techniques for detecting instances from object categories in still images consist of looping over a partition of a pose space with dedicated binary classifiers. The efficiency of this strategy for a complex pose, that is, for fine-grained descriptions, can be assessed by measuring the effect of sample size and pose resolution on accuracy and computation. Two conclusions emerge: (1) fragmenting the training data, which is inevitable in dealing with high in-class variation, severely reduces accuracy; (2) the computational cost at high resolution is prohibitive due to visiting a massive pose partition.