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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Texture Classification Using Dense Micro-block Difference (DMD)

verfasst von : Rakesh Mehta, Karen Egiazarian

Erschienen in: Computer Vision -- ACCV 2014

Verlag: Springer International Publishing

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Abstract

The paper proposes a novel image representation for texture classification. The recent advancements in the field of patch based features compressive sensing and feature encoding are combined to design a robust image descriptor. In our approach, we first propose the local features, Dense Micro-block Difference (DMD), which capture the local structure from the image patches at high scales. Instead of the pixel we process the small blocks from images which capture the micro-structure from it. DMD can be computed efficiently using integral images. The features are then encoded using Fisher Vector method to obtain an image descriptor which considers the higher order statistics. The proposed image representation is combined with linear SVM classifier. The experiments are conducted on the standard texture datasets (KTH-TIPS-2a, Brodatz and Curet). On KTH-TIPS-2a dataset the proposed method outperforms the best reported results by \(5.5\,\%\) and has a comparable performance to the state-of-the-art methods on the other datasets.

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Vorheriges Kapitel Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning
Nächstes Kapitel Nuclear- Norm Joint Regression for Face Reconstruction and Recognition
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Metadaten
Titel
Texture Classification Using Dense Micro-block Difference (DMD)
verfasst von
Rakesh Mehta
Karen Egiazarian
Copyright-Jahr
2015
Buch
Computer Vision -- ACCV 2014

Print ISBN: 978-3-319-16807-4

Electronic ISBN: 978-3-319-16808-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16808-1_43

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