Pattern Recognition and Image Analysis

, Volume 17, Issue 1, pp 11–24

A new texture representation approach based on local feature saliency

  • M. K. Bashar
  • N. Ohnishi
  • K. Agusa
Image Processing, Analysis, Recognition, and Understanding

DOI: 10.1134/S1054661807010038

Cite this article as:
Bashar, M.K., Ohnishi, N. & Agusa, K. Pattern Recognit. Image Anal. (2007) 17: 11. doi:10.1134/S1054661807010038

Abstract

Although it has been studied in some depth, texture characterization is still a challenging issue for real-life applications. In this study, we propose a multiresolution salient-point-based approach in the wavelet domain. This incorporates a two-phase feature extraction scheme. In the first phase, each wavelet subband (LH, HL, or HH) is used to compute local features by using multidisciplined (statistical, geometrical, or fractal) existing texture measures. These features are converted into binary images, called salient point images (SPIs), via threshold operation. This operation is the key step in our approach because it provides an opportunity for better segmentation and combination of multiple features. In the final phase, we propose a set of new texture features, namely, salient-point density (SPD), non-salient-point density (NSPD), salient-point residual (SPR), saliency and non-saliency product (SNP), and salient-point distribution non-uniformity (SPDN). These features characterize various aspects of image texture such as fineness/coarseness, primitive distribution, internal structures, etc. These features are then applied to the well-known K-means algorithm for unsupervised segmentation of texture images. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potential of the proposed features compared to the wavelet energy (WE) and local extrema density feature (LED).

Keywords

texturewavelet transformfeature saliencybinary domain texture featuressegmentation

Copyright information

© Pleiades Publishing, Ltd. 2007

Authors and Affiliations

  • M. K. Bashar
    • 1
  • N. Ohnishi
    • 2
  • K. Agusa
    • 1
  1. 1.Department of Information Engineering, Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Department of Media Science, Graduate School of Information ScienceNagoya UniversityNagoyaJapan