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Texture is an important visual clue for various classification and segmentation tasks in the scene understanding challenge. Today, successful deployment of deep learning algorithms for texture recognition leads to tremendous precisions on standard datasets. In this paper, we propose a new learning framework to train deep neural networks in parallel and with variable depth for texture recognition....
Machine learning is a very promising way of solving some image processing tasks. However, existing approaches fails at integrating feature selection within the learning task. This paper introduces a new two stage learning algorithm called near infinitely linear combination (NILC) that performs at the same time variable selection and error minimization. Empirical evidence reported on different document...
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