Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
نویسندگان
چکیده
In this paper we propose and examine non–parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi–scale Gabor filter bank. We will demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer an unified approach to these closely related tasks. We present results on Brodatz–like micro–textures and a collection of real–word images.
منابع مشابه
Unsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملTexture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions
We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than o...
متن کاملEmpirical Evaluation of Dissimilarity Measures for Color and Texture
This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color, and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, ...
متن کاملUnsupervised Natural Image Segmentation Using Mean Histogram Features
A new histogram feature based natural image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. A non parametric Bayesean clustering is employed to make the segmentation framework fully unsupervised where no a priori knowledge about the number...
متن کامل