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Texture Classification Using Dense Micro-block Difference (DMD)

  • Rakesh MehtaEmail author
  • Karen Egiazarian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

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.

Keywords

Local Feature Patch Size Gaussian Mixture Model Local Binary Pattern Texture Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

The research leading to this paper partially received funding from TUT project Big Data 83255.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Tampere University of TechnologyTampereFinland

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