Sparse Multiscale Local Binary Patterns

In a Local Binary Pattern (LBP) representation, circular point features are taken in their entirety as predicates and restricted to uniform patterns with limited scales of small numbers of features in order to avoid large bin complexity. Such a design cannot fully exploit the discriminative capacity of the features available. To address the problem, this paper proposes (1) a pairwise-coupled reformulation of LBP-type classification which involves selecting single-point features for each pair of classes across multiple scales to form compact, contextually-relevant multiscale predicates known as Multiscale Selected Local Binary Features (MSLBF), and (2) a novel binary feature selection procedure, known as Binary Histogram Intersection Minimisation (BHIM) designed to choose features with minimal redundancy. Experiments show the advantages of MSLBF over traditional LBP representation and of BHIM over feature selection schemes such as AdaBoost.