Robust fuzzy local information and ‐norm distance‐based image segmentation method

Li, Fang; Qin, Jing

doi:10.1049/iet-ipr.2016.0539

Cited by 14 publications

(5 citation statements)

References 35 publications

(51 reference statements)

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“…The new 8 components based on bins are computed using Eqs. (13) to (20). These eight components are named as a, b, c, d, e, f, g and h.…”

Section: Cpmid Descriptormentioning

confidence: 99%

“…The CPMID descriptor is used to segment the input image for detecting the building objects. Few algorithms are already released based on the variations in Fuzzy C-Means algorithm (FCM) such as Fuzzy Double C-Means based on sparse selfrepresentation (FDCM_SSR) [19], Fuzzy local information LP (FLILP) clustering [20], Generalized FCM clustering algorithm with local information (GFCMLI) [21] and Adaptive fuzzy local information C-means (ADFLICM) [22]. This paper introduces a novel image segmentation method namely Morphological Operations Induced FCM (MOI-FCM) which is the modified version of Fuzzy C-Means algorithm.…”

Section: Morphological Operations Induced Fcmmentioning

confidence: 99%

“…This algorithm is applied in data clustering and image segmentation. F. Li et al [20] provides a variant of FCM clustering algorithm called as Fuzzy Local Information LP (FLILP) clustering for image segmentation. This algorithm outperforms in terms of noise immunity and segmentation accuracy compared to other existing variants of FCM.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Building Detection Technique Using Image Descriptor CPMID in Urban Area

Singarayan¹,

Kamalam²

2019

IJIES

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Building Detection in urban areas is a crucial and challenging part of satellite imagery but it suffers the issue of inaccuracy. This research proposes a novel texture descriptor for segmentation of buildings in urban areas. This texture descriptor is designed and derived from the practical assessment of Chess piece movements telescoped with King, Queen, Bishop, Knight, and Rook and henceforth named as Chess Piece Movement based Image Descriptor (CPMID). The proposed method is hence named as Building Detection using CPMID; FCM and Morphology operations (BD-CPMIDFM). The proposed descriptor is empowered with data acquisition in all permitted directions of the specified Chess pieces which provides the texture image descriptor with higher accuracy that can be used in building detection in urban areas. The building detection is achieved through Morphology Operations Induced Fuzzy C-Means (MOI-FCM) segmentation and this output is post-processed through morphological Erosion and Dilation operations. The proposed building detection method shows significant levels of improvement in accuracy than existing versions in literature. Experimental results show that the proposed method improves the average segmentation accuracy up to 91.514.

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“…The new 8 components based on bins are computed using Eqs. (13) to (20). These eight components are named as a, b, c, d, e, f, g and h.…”

Section: Cpmid Descriptormentioning

confidence: 99%

Section: Morphological Operations Induced Fcmmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Building Detection Technique Using Image Descriptor CPMID in Urban Area

Singarayan¹,

Kamalam²

2019

IJIES

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“…Simultaneously, it is also a fundamental step for further image understanding [1] and directly affects the accuracy of many computer vision applications [2], such as object detection [3], object tracking [4,5] and image retrieval [6]. In general, existing color image segmentation methods can be classified into five categories: thresholding, clustering, edge detection, region extraction and saliency detection [7]. These methods are mainly based on watershed transform or clustering procedures [8].…”

Section: Introductionmentioning

confidence: 99%

K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Yang

Wen

et al. 2018

Symmetry

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Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

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“…The approach improves FCM segmentation results solves the problem of blocky artifacts and also edge preservation by incorporating denoising and segmentation in one package. A modified version of FCM is proposed in [79] in which L p norm is used instead of L 2 for distance calculation. Also, spatial and color information are incorporated into the objective function to deal with noise and outliers.…”

Section: Fcm-based Noisy Image Segmentationmentioning

confidence: 99%

Particle Swarm Optimization and Fuzzy C-means for Domain-independent Noisy Image Segmentation

Mirghasemi¹

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<p>Image segmentation is considered to be one of the foremost image analysis techniques for high-level real-world applications in computer vision. Its task is to change or simplify the representation of an image in order to make it easier to understand or analyze. Although image segmentation has been studied for many years, evolving technology and transformation of demands make image segmentation a continuing challenge. Noise as a side effect of imaging devices is an inevitable part of images in many computer vision applications. Therefore, an important topic in image segmentation is noisy image segmentation which requires extra effort to deal with image segmentation in the presence of noise. Generally, different strategies are needed for different noisy images with different levels/types of noise. Therefore, many approaches in the literature are domain-dependent and applicable only to specific images. A well-recognized approach in noisy image segmentation uses clustering algorithms, among which Fuzzy C-Means (FCM) is one of the most popular. FCM is unsupervised, efficient, and can deal with uncertainty and complexity of information in an image. Dealing with uncertainties is easier with the fuzzy characteristic of FCM, and complexity of information is being taken care of by utilizing different features in FCM, and also combining FCM with other techniques. Many modifications have been introduced to FCM to deal with noisy image segmentation more effectively. Common approaches include, adding spatial information into the FCM process, addressing the FCM initialization problem, and enhancing features used for segmentation. However, existing FCM-based noisy image segmentation approaches in the literature generally suffer from three drawbacks. First, they are applicable to specific domains and images, and impotent in others. Second, they don’t perform well on severely noisy image segmentation. Third, they are effective on specific type and level of noise, and they don’t explore the effect of noise level variation. Recently, evolutionary computation techniques due to their global search abilities have been used in hybridization with FCM, mostly to address FCM stagnation in local optima. Particle Swarm Optimization (PSO) is particularly of interest because of its lower computational costs, easy implementation, and fast convergence, but its potential in this area has not been fully investigated. This thesis develops new domain-independent PSO-based algorithms for an automatic non-supervised FCM-based segmentation of severely noisy images which are capable of extracting the main coherent/homogeneous regions while preserving details and being robust to noise variation. The key approach taken in the thesis is to explore the use of PSO to manipulate and enhance local spatial and spatial-frequency information. This thesis introduces a new PSO feature enhancement approach in wavelet domain for noisy image segmentation. This approach applies adaptive wavelet shrinkage using evaluation based on FCM clustering performance. The results show great accuracy in the case of severe noise because of the enhanced features. Also, due to adaptivity, no parameter-tuning is required according to the type or volume of noise, and the performance is consistent under noise level variation. This thesis presents a scheme under which a fusion of two different denoising algorithms for more effective segmentation is possible. This fusion retains the advantages of each algorithm while leaving out their drawbacks. The fusion scheme uses the noisy image segmentation system introduced above and anisotropic diffusion, the edge-preserving denoising algorithm. Results show greater accuracy and stability in comparison to the individual algorithms on a variety of noisy images. This thesis introduces another PSO-based edge-preserving adaptive wavelet shrinkage system using wavelet packets, bilateral filtering, and a detail-respecting shrinkage scheme. The analysis of the results provide a comparison between the two feature enhancement systems. The first system uses wavelets and the second uses wavelet packets as a domain to enhance features for an FCM-based noisy image segmentation. Also, the highest segmentation accuracy among all the algorithms introduced in this thesis on some benchmarks belong to this system.</p>

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Robust fuzzy local information and ‐norm distance‐based image segmentation method

Cited by 14 publications

References 35 publications

A Novel Building Detection Technique Using Image Descriptor CPMID in Urban Area

A Novel Building Detection Technique Using Image Descriptor CPMID in Urban Area

K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Particle Swarm Optimization and Fuzzy C-means for Domain-independent Noisy Image Segmentation

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