You are not logged in.

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,MFHORCID iD for She,MFH orcid.org/0000-0001-8191-0820
Kong,LORCID iD for 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

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 156 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Sun, 08 Mar 2015, 21:42:53 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.