Joint Color-Spatial-Directional Clustering and Region Merging (JCSD-RM) for Unsupervised RGB-D Image Segmentation

Hasnat, Abul; Alata, Olivier; Trémeau, Alain

doi:10.1109/tpami.2015.2513407

Cited by 36 publications

(22 citation statements)

References 38 publications

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“…The membership formula obtained by substituting (22) into (19) is as follows: (23) where:ȳ r is the mean value of the bias field in the neighborhood of the pixel point x k .…”

Section: Kernel-induced Distance Based Kbfwcm With Dpatial Constraintsmentioning

confidence: 99%

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Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

et al. 2019

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The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.

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“…The membership formula obtained by substituting (22) into (19) is as follows: (23) where:ȳ r is the mean value of the bias field in the neighborhood of the pixel point x k .…”

Section: Kernel-induced Distance Based Kbfwcm With Dpatial Constraintsmentioning

confidence: 99%

“…Image segmentation has always been one of the most challenging tasks in image processing and computer vision [20,21]. Nowadays, there have been many types of methods for image segmentation [22][23][24][25], however, these methods are all not robust and effective enough for a large number of different images.…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

et al. 2019

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“…Therefore, it is necessary to merge the over-segmented regions to obtain the constitutive elements of the image. The iterative scheme proposed in [10] requires, first of all, the construction of an adjacency graph RAG = (V, E) for region merging. This graph takes each segmented…”

Section: Region Mergingmentioning

confidence: 99%

“…The strategy to merge the regions proposed in [10] consists of an iterative procedure that evaluates a merging predicate between the adjacent nodes. The candidacy of a region defines if the region is valid to be merged with its neighboring nodes.…”

Section: Merging Strategymentioning

confidence: 99%

Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging

Lorenti

Giacomantone

Bria

2018

JC&ST

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Time of Flight (TOF) cameras generate two simultaneous images, one of intensity and one of range. This allows to tackle segmentation problems in which the separate use of intensity or range information is not enough to extract objects of interest from the 3D scene. In turn, range information allows to obtain a normal vector estimation of each point of the captured surfaces. This article presents a semi-supervised spectral clustering method which combines intensity and range information as well as normal vector orientations to improve segmentation results. The main contribution of this article consists in the use of a statistical region merging as a final step of the segmentation method. The region merging process combines adjacent regions which satisfy a similarity criterion. The performance of the proposed method was evaluated over real images. The use of this final step presents preliminary improvements in the metrics evaluated.

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“…Algorithms based on edges [7,9,10] achieve good performance on images where the boundary of the object is distinct, but these methods are less resistant to noise and require higher image quality. Region-based [11][12][13] and threshold-based [14][15][16] segmentation methods merge pixels into regions by their features like color, texture, or their combination. However, for a certain image, there are some redundant features which do not play a role in segmentation.…”

Section: Introductionmentioning

confidence: 99%

Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

Wei

Shen

2018

Advances in Multimedia

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Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.

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Joint Color-Spatial-Directional Clustering and Region Merging (JCSD-RM) for Unsupervised RGB-D Image Segmentation

Cited by 36 publications

References 38 publications

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging

Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

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