Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns

• Published in: Computer vision and image understanding Vol. 142; pp. 13 - 22
• Main Authors: Sharma, Gaurav, Jurie, Frédéric
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2016; Elsevier
• Subjects: Computer Science; Computer Vision and Pattern Recognition; Face verification; Image classification; Local features; Texture categorization; Local features; Texture categorization; Face verification; Image classification; local features; face verification; texture categorization; image classification
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2015.09.007

•We propose a new image representation for texture categorization and facial analysis.•The proposed representation exploits higher order statistics of non-binarized local pixel patterns.•It avoids limitations of previous methods such as hard quantization, counting statistics and heuristic pruning of feature space.•We demonstrate effectiveness with extensive experiments on four benchmark datasets. We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that 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, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance.