Support Discrimination Dictionary Learning for Image Classification
Dictionary learning has been successfully applied in image classification. However, many dictionary learning methods that encode only a single image at a time while training, ignore correlation and other useful information contained within the entire trai
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Computer Laboratory, University of Cambridge, Cambridge, UK {yl504,wc253,ijw24}@cam.ac.uk 2 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China 3 Department of Electronic and Electrical Engineering, University College London, London, UK [email protected]
Abstract. Dictionary learning has been successfully applied in image classification. However, many dictionary learning methods that encode only a single image at a time while training, ignore correlation and other useful information contained within the entire training set. In this paper, we propose a new principle that uses the support of the coefficients to measure the similarity between the pairs of coefficients, instead of using Euclidian distance directly. More specifically, we proposed a support discrimination dictionary learning method, which finds a dictionary under which the coefficients of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. In addition, adopting a shared dictionary in a multi-task learning setting, this method can find the number and position of associated dictionary atoms for each class automatically by using structured sparsity on a group of images. The proposed model is extensively evaluated using various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods.
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Introduction
Sparse representation has been successfully applied to a variety of problems in image processing and computer vision, e.g., image denoising, image restoration and image classification. In the framework of sparse representation, an image can be represented as a linear combination of a few bases selected sparsely from an over-complete dictionary. The dictionaries can be predefined by the use of some off-the-shelf basis, such as the Discrete Fourier Transform (DFT) matrix and the wavelet matrix. However, it has been shown that learning the dictionary from the training data enables a more sparse representation of the image in comparison to using a predefined one, which can lead to improved performance in the reconstruction task. Some typical reconstruction dictionary learning methods include the Method of optimal direction (MOD) [1], and K-SVD [2]. Sparse representation has also been considered in pattern recognition applications. For example, it has been used in the Sparse representation classifier c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part II, LNCS 9906, pp. 375–390, 2016. DOI: 10.1007/978-3-319-46475-6 24
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(SRC) [3], which achieves competitive recognition performance in face recognition. In contrast to image reconstruction which only concerns the sparse representation of an image, in pattern recognition, the main goal is to find the correct label for the query sample, consequently the discriminative capability of the learned dictionary is crucial. A variety of discriminative dictionary learning methods have recently been prop
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