Unsupervised Image Segmentation based on the Multi-resolution Integration of Adaptive Local Texture Descriptors

The major aim of this paper consists of a comprehensive quantitative evaluation of adaptive texture descriptors when integrated into an unsupervised image segmentation framework. The techniques involved in this evaluation are: the standard and rotation invariant Local Binary Pattern (LBP) operators, multichannel texture decomposition based on Gabor filters and a recently proposed technique that analyses the distribution of dominant image orientations at both micro and macro levels. These selected descriptors share two essential properties: (a) they evaluate the texture information at micro-level in small neighborhoods, while (b) the distributions of the local features calculated from texture units describe the texture at macrolevel. This adaptive scenario facilitates the integration of the texture descriptors into an unsupervised clustering based segmentation scheme that embeds a multi-resolution approach. The conducted experiments evaluate the performance of these techniques and also analyze the influence of important parameters (such as scale, frequency and orientation) upon the segmentation results.

[1]  Paul F. Whelan,et al.  Multi-resolution Texture Classification Based on Local Image Orientation , 2008, ICIAR.

[2]  John Daugman,et al.  Neural networks for image transformation, analysis, and compression , 1988, Neural Networks.

[3]  Kenneth I. Laws,et al.  Rapid Texture Identification , 1980, Optics & Photonics.

[4]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[5]  Martial Hebert,et al.  Measures of Similarity , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[6]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Paul F. Whelan,et al.  Evaluation of Local Orientation for Texture Classification , 2008, VISAPP.

[10]  Michal Strzelecki,et al.  Texture Analysis Methods - A Review , 1998 .

[11]  Joachim M. Buhmann,et al.  Unsupervised Texture Segmentation in a Deterministic Annealing Framework , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Trygve Randen,et al.  Texture segmentation using filters with optimized energy separation , 1999, IEEE Trans. Image Process..

[13]  Paul F. Whelan,et al.  CTex-An Adaptive Unsupervised Segmentation Algorithm based on Colour-Texture Coherence , 2022 .

[14]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[15]  Joachim M. Buhmann,et al.  Empirical Evaluation of Dissimilarity Measures for Color and Texture , 2001, Comput. Vis. Image Underst..

[16]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[17]  Matti Pietikäinen,et al.  Unsupervised texture segmentation using feature distributions , 1997, Pattern Recognit..