Image segmentation based on multiresolution Markov random field with fuzzy constraint in wavelet domain

Chen, Zheng; Qin, Qian; Liu, G.; Hu, Yaosi

doi:10.1049/iet-ipr.2010.0176

Cited by 18 publications

(15 citation statements)

References 16 publications

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“…(4) to (6). A set of main peaks with the maximum η is selected as the initial segmentation threshold value.…”

Section: Outputmentioning

confidence: 99%

An adaptive clustering segmentation algorithm based on FCM

Yang¹,

Ke²,

Wang³

2017

Turk J Elec Eng & Comp Sci

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Abstract:The cluster number and the initial clustering centers must be reasonably set before the analysis of clustering in most cases. Traditional clustering segmentation algorithms have many shortcomings, such as high reliance on the specially established initial clustering center, tendency to fall into the local maximum point, and poor performance with multithreshold values. To overcome these defects, an adaptive fuzzy C-means segmentation algorithm based on a histogram (AFCMH), which synthesizes both main peaks of the histogram and optimized Otsu criterion, is proposed.First, the main peaks of the histogram are chosen by operations like histogram smoothing, merging of adjacent peaks, and filtering of small peaks, and then the values of main peaks are calculated. Second, a new separability measure η is defined and a group of main peaks with the maximum value of η serve as the optimal segmentation threshold value. The values of these main peaks are employed for initializing of the initial clustering center. Finally, the image is segmented by the weighted fuzzy C-means clustering algorithm. The experiment results show that, compared with existing algorithms, the proposed method not only avoids the oversegmentation phenomenon but also has a significantly shorter computing time than the traditional segmentation algorithm based on mean shift. Therefore, the proposed algorithm can obtain satisfactory results and effectively improve executive efficiency.

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“…(4) to (6). A set of main peaks with the maximum η is selected as the initial segmentation threshold value.…”

Section: Outputmentioning

confidence: 99%

An adaptive clustering segmentation algorithm based on FCM

Yang¹,

Ke²,

Wang³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…For comparison, apart from our method, the performances of the competing approaches are also considered, i.e., support vector machine (SVM) [43], iterated conditional mode (ICM) [26], MRMRF with fuzzy constraint (MRMRF-F) [29], iterative region growing using semantics (IRGS) [33], and OMRF without regional penalties. The SVM is a classical classification method with training.…”

Section: B Segmentation Of Synthetic Imagesmentioning

confidence: 99%

“…The multiresolution MRF (MRMRF) model is exploited to extend the classical MRF model by incorporating interactions in a large neighborhood [28], [29]. It uses the quad-tree or wavelet transform to build the pyramid structure of an image [ Fig.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Model With Regional Penalties

Chen

Wang

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes a novel object-based Markov random field model (OMRF) for semantic segmentation of remote sensing images. First, the method employs the region size and edge information to build a weighted region adjacency graph (WRAG) for capturing the complicated interactions among objects. Thereafter, aimed at modeling object interactions in the OMRF, the size and edge information are further introduced into the Gibbs joint distribution of the random field as regional penalties. Finally, the semantic segmentation is achieved through a principled probabilistic inference of the OMRF with regional penalties. The proposed method is compared with other MRFbased methods and some state-of-the-art methods. Experiments are conducted on a series of synthetic and real-world images. Segmentation results demonstrate that our method provides better performance (an accuracy improvement about 3%). Moreover, we further discuss the application of the proposed method for classification.Index Terms-Object-based Markov random field (OMRF), regional penalties, remote sensing images, semantic segmentation.

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“…With the development of multiresolution analysis, the image procedure methods based on multiresolution analysis are proposed (Chang and Kuo, 1993;Jung, 2007;Zheng et al, 2012). The proposed methods use multiresolution analysis to extract multiple features, and bring them into image segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Energy Segmentation Method of High-Resolution Sar Image Based on Multiple Features

Wang

You

Liu

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. To achieve the optimal image segmentation, an energy segmentation method based on multiple features and blocks for high resolution Synthetic Aperture Radar (SAR) image is proposed in this paper. First of all, a feature vector of pixel is formed with the texture feature extracted by curvelet transform and means function, the boundary feature extracted by curvelet transform and Canny Operator, and the original spectral feature; a feature set is formed by all feature vectors of pixels in the image. The feature vector is considered as segmentation basis, and its domain is partitioned by regular tessellation. On the partitioned image domain, a label variable is assigned to a regular block; each homogeneous region is fitted by one or more regular blocks; Obviously, a label field is formed by all the label variables of regular blocks. The model of label field is built by using energy function of neighborhood relationship. The feature set is considered as a realization of a random filed of multiple features (multiple features field for short). A heterogeneous energy function is used to establish the model of multiple features field. Then the established models of the label field and multiple features field are combined to define global energy function of image segmentation, and non-constrained Gibbs probability distribution is used to describe the global energy function to build the energy segmentation model based on multiple features. Further, a RJMCMC algorithm is designed to simulate from the model to segment SAR image. To verify the feasibility and superiority of the proposed approach, real SAR images are tested.

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Image segmentation based on multiresolution Markov random field with fuzzy constraint in wavelet domain

Cited by 18 publications

References 16 publications

An adaptive clustering segmentation algorithm based on FCM

An adaptive clustering segmentation algorithm based on FCM

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Model With Regional Penalties

An Energy Segmentation Method of High-Resolution Sar Image Based on Multiple Features

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