Sparse representation with multi-manifold analysis for texture classification from few training images Sun,X, Wang,J, She,MFH and Kong,L 2014, Sparse representation with multi-manifold analysis for texture classification from few training images, Image and vision computing, vol. 32, no. 11, pp. 835-846, doi: 10.1016/j.imavis.2014.07.001. Attached Files Name Description MIMEType Size Downloads Title Sparse representation with multi-manifold analysis for texture classification from few training images Author(s) Sun,X Wang,J She,MFH orcid.org/0000-0001-8191-0820 Kong,L orcid.org/0000-0001-6219-3897 Journal name Image and vision computing Volume number 32 Issue number 11 Start page 835 End page 846 Total pages 12 Publisher Elsevier Place of publication Amsterdam , Netherlands Publication date 2014-11 ISSN 0262-8856 Keyword(s) Few training image Manifold learning Multi-manifold analysis Sparse representation Texture classification Science & Technology Technology Physical Sciences Computer Science, Artificial Intelligence Computer Science, Software Engineering Computer Science, Theory & Methods Engineering, Electrical & Electronic Optics Computer Science Engineering LOCAL BINARY PATTERNS FACE RECOGNITION FEATURES ROTATION CATEGORIES WAVELET SCALE Summary Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved. Language eng DOI 10.1016/j.imavis.2014.07.001 Field of Research 080104 Computer Vision Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture) HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2014, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30070361 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Institute for Frontier Materials GTP Research Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Sat, 07 Mar 2015, 22:56:02 EST Sun, 08 Mar 2015, 21:42:53 EST Mon, 09 Mar 2015, 04:00:04 EST Mon, 09 Mar 2015, 07:23:51 EST Wed, 25 Mar 2015, 14:26:33 EST Sat, 28 Mar 2015, 05:08:15 EST Mon, 30 Mar 2015, 10:42:47 EST Mon, 30 Mar 2015, 10:44:05 EST Mon, 30 Mar 2015, 10:47:42 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 524 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Sun, 08 Mar 2015, 21:42:53 EST