Abstract
In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.
Highlights
Image segmentation is one of the key techniques in the field of remote sensing and computer vision
The reminder of this paper is organized as follows: in Section 2, we provide a brief review of the region-level markov random field model
Though image segmentation based on region-level Markov random field model (RMRF) model can take into account the large range of spatial information through a priori probability function at the region level, the model only describes the statistical characteristics of the image on a single spatial resolution scale, which is obviously incompatible with the non-stationary characteristics of the image, and needs to be improved
Summary
Image segmentation is one of the key techniques in the field of remote sensing and computer vision. Chatzis proposed a novel robust fuzzy local c-means algorithm (FLICM), where a fuzzy factor was introduced into its objective function to guarantee noise insensitiveness and image detail preservation [4] This method was lately improved by M.Gongfrom introducing a kernel distance measure into its objective function [5]. Markov random field (HMRF) model in the fuzzy clustering procedure and proposed the HMRF-FCM algorithm [8] We extended this method into a multi-scale version by capturing and utilizing the multi-scale spatial constrains [9] and enhanced its accuracy of selecting local information by incorporating region-level information into the fuzzy clustering [10]. The region-level MRF (RMRF) [11] models introduce the interregional interaction into the image segmentation, which will make full use of the larger scope of the image space information.
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