Sparse representation with multi-manifold analysis for texture classification from few training images

Journal article


Sun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue. (2014). Sparse representation with multi-manifold analysis for texture classification from few training images. Image and Vision Computing. 32(11), pp. 835 - 846. https://doi.org/10.1016/j.imavis.2014.07.001
AuthorsSun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue
Abstract

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.

Keywordstexture classification; sparse representation; manifold learning; multi-manifold analysis; few training image
Year2014
JournalImage and Vision Computing
Journal citation32 (11), pp. 835 - 846
PublisherElsevier Ltd
ISSN0262-8856
Digital Object Identifier (DOI)https://doi.org/10.1016/j.imavis.2014.07.001
Scopus EID2-s2.0-84906552374
Page range835 - 846
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationUnited Kingdom
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