Support discrimination dictionary learning for image classification


Type
Conference Object
Change log
Authors
Liu, Y 
Chen, W 
Chen, Q 
Abstract

© 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.

Description
Keywords
4605 Data Management and Data Science, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name
14th European Conference on Computer Vision (ECCV 2016)
Journal ISSN
0302-9743
1611-3349
Volume Title
9906 LNCS
Publisher
Springer International Publishing
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)