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