Abstract
Accurate image segmentation is an important issue in image processing, where Gaussian mixture models play an important part and have been proven effective. However, most Gaussian mixture model (GMM) based methods suffer from one or more limitations, such as limited noise robustness, over-smoothness for segmentations, and lack of flexibility to fit data. In order to address these issues, in this paper, we propose a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation. First, based on our previous work where each cluster is characterized by three automatically determined rough-fuzzy regions, we partition the target image into three rough regions with two adaptively computed thresholds. Second, a new bounded indicator function is proposed to determine the bounded support regions of the observed data. The bounded indicator and posterior probability of a pixel that belongs to each sub-region is estimated with respect to the rough region where the pixel lies. Third, to further reduce over-smoothness for segmentations, two novel prior factors are proposed that incorporate the spatial information among neighborhood pixels, which are constructed based on the prior and posterior probabilities of the within- and between-clusters, and considers the spatial direction. We compare our algorithm to state-of-the-art segmentation approaches in both synthetic and real images to demonstrate the superior performance of the proposed algorithm.
Highlights
As one of the classical problems in image processing, image segmentation has been extensively studied, which can be treated as a classification problem [1,2,3,4,5] for the target image
Because bounded asymmetric mixture model (BAMM) [19] has already proven its superior performance over Gaussian mixture model (GMM), s-t mixture model (SMM), and generalized Gaussian mixture model (GGMM), we only present the estimated distributions obtained by employing f(xi|μk, Sk) (i.e., the Gaussian distribution in the GMM model, Eq (2)), Ψ(xi|μkl, Skl) (i.e., the bounded Gaussian distribution in BAMM model, Eq (10)), and Ψ~ ðxijmkl; SklÞ (i.e. the proposed distribution, Eq (17))
To overcome the limitations involved in most GMM-based algorithms, in this paper, we proposed a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation
Summary
As one of the classical problems in image processing, image segmentation has been extensively studied, which can be treated as a classification problem [1,2,3,4,5] for the target image. Various image segmentation algorithms have been developed such as active contour models [6, 7], graph based methods [8, 9] and clustering techniques [10,11,12]. Over the last decades, modelbased techniques [13, 14] have been widely used in image segmentation, where the standard Gaussian mixture model (GMM) [15, 16] is a well-known method because of its simplicity and ease of implementation [17]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
