Support discrimination dictionary learning for image classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Liu, Y., Chen, W., Chen, Q., & Wassell, I. (2016). Support discrimination dictionary learning for image classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9906 LNCS 375-390. https://doi.org/10.1007/978-3-319-46475-6_24
© Springer International Publishing AG 2016. 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.
External DOI: https://doi.org/10.1007/978-3-319-46475-6_24
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274242