Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification

PARSE representations have become popular in several applications of signal, image and video processing, such as denoising [1], [2], super-resolution, inpainting, compression [3]-[6] or classification. While it was common to analyze and reconstruct signals based on representations over predefined bases such as wavelets and DCT, research in the recent years has shown that learning overcomplete dictionaries adapted to the structure of the treated signals can significantly improve the representation quality. Observing that learning redundant dictionaries from collections of data samples under sparsity priors leads to models that fit and approximate well the characteristics of signals [7], [8], the learning of dictionaries in a supervised setting for the discrimination of different classes of signals has also become a popular research problem [9]. In this work, we propose a method to learn multilevel structured dictionaries with high discrimination capability for the problem of pixelwise image classification. We consider a supervised classification setting where the classes are known and exemplars are available for each class. In particular, we are interested in image classification problems with a large amount of variability between data samples of the same class, resulting from e.g., dominant presence