Local Higher-Order Statistics (LHS) – A Novel Image Representaion for Texture Categorization and Facial Analysis
Résumé
We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute. Motivated by such works, the proposed model employs higher-order statistics of local non-binarized pixel patterns for the image description. Hence, in addition to being remarkably simple, it requires neither any user specified quantization of the space (of pixel patterns) nor any heuristics for discarding low occupancy volumes of the space. This leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets outperforming contemporary methods, with similar complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance.
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