| Texture Classification from Random Features. | |
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MedLine Citation:
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PMID: 21768653 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform texture classification, thus learning and classification are carried out in a compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on both the CUReT, the Brodatz and the MSRC databases, comparing the proposed approach to four state-of-the-art texture classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality. |
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Authors:
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Li Liu; Paul Fieguth |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-7-13 |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: - ISSN: 1939-3539 ISO Abbreviation: - Publication Date: 2011 Jul |
Date Detail:
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Created Date: 2011-7-19 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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National University of Defense Technology, Changsha. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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