As a recently proposed technique, sparse representation based classification (SRC)

Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by IntroductionThe past several years have witnessed the rapid development of the theory and algorithms of sparse representation (or coding) [30] and its successful applications in image restoration [1][2][3] and compressed sensing [4]. Recently sparse representation techniques have also led to promising results in image classification, e.g. face recognition (FR) [5-7, 10, 31], digit and texture classification [8][9][11][12], etc. The success of sparse representation based classification owes to the fact that a high-dimensional image can be represented or coded by a few representative samples from the same class in a low-dimensional manifold, and the recent progress of l 0 -norm and l 1 -norm minimization techniques [28].In sparse representation based classification, there are two phases: coding and classification. First, the query signal/image is collaboratively coded over a dictionary of atoms with some sparsity constraint, and then classification is performed based on the coding coefficients and the dictionary. The dictionary for sparse coding could be predefined. For example, Wright et al. [5] directly used the training samples of all classes as the dictionary to code the query face image, and classified the query face image by evaluating which class leads to the minimal reconstruction error. Although this so called sparse representation based classification (SRC) scheme shows interesting FR results, the dictionary used in it may not be effective enough to represent the query images due to the uncertain and noisy information in the original training images. The number of atoms of such a dictionary can also be very big, which increases the coding complexity. In addition, using the original training samples as the dictionary could not fully exploit the discriminative information hidden in the training samples. On the other hand, using analytically designed off-the-shelf bases as dictionary (e.g., [8] uses Haar wavelets and Gabor wavelets as the dictionary) might be universal to all types of images but will not be effective enough for specific type of images such as face, digit and texture images. In fact, all the above mentioned problems of predefined dictionary can be addressed, at least to some extent, by learning properly a non...

Image-based virtual try-on systems for fitting a new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. A desirable pipeline should not only transform the target clothes into the most fitting shape seamlessly but also preserve well the clothes identity in the generated image, that is, the key characteristics (e.g. texture, logo, embroidery) that depict the original clothes. However, previous image-conditioned generation works fail to meet these critical requirements towards the plausible virtual try-on performance since they fail to handle large spatial misalignment between the input image and target clothes. Prior work explicitly tackled spatial deformation using shape context matching, but failed to preserve clothing details due to its coarse-to-fine strategy. In this work, we propose a new fully-learnable Characteristic-Preserving Virtual Try-On Network (CP-VTON) for addressing all real-world challenges in this task. First, CP-VTON learns a thin-plate spline transformation for transforming the in-shop clothes into fitting the body shape of the target person via a new Geometric Matching Module (GMM) rather than computing correspondences of interest points as prior works did. Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness. Extensive experiments on a fashion dataset demonstrate our CP-VTON achieves the state-ofthe-art virtual try-on performance both qualitatively and quantitatively. Code is available at https://github.com/sergeywong/cp-vton.

Recently the sparse representation (or coding) based classification (SRC)

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