Unsupervised Image Segmentation via Affinity Propagation

2014 ◽  
Vol 610 ◽  
pp. 464-470 ◽  
Author(s):  
Wei Fu Peng ◽  
Shu Du ◽  
Fu Xiang Li
Keyword(s):  
Image Segmentation ◽  
Natural Images ◽  
Affinity Propagation ◽  
Complex Nature ◽  
Segmentation Method ◽  
Local Texture ◽  
Color Features ◽  
Segmentation Methods ◽  
Complex Scenes ◽  
Unsupervised Image Segmentation

Image segmentation is an important research subject in the area of image processing. Most of the existing image segmentation methods partition the image based on the single cue of the image, the color, which brings a serious limitation when the complex scenes involve in the natural images. In this paper, we introduce a novel unsupervised image segmentation method via affinity propagation which takes into local texture and color features with superpixel map. The new method fuses color and texture information as local feature of each superpixel. The experimental results show that the proposed method performs better and steadier when partitioning various complex nature images, comparing to the existing methods.

scholarly journals Aerial Image Segmentation with Clustering Using Fireworks Algorithm

2019 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Muhammad Hariz Arasy ◽  
Suyanto Suyanto ◽  
Kurniawan Nur Ramadhani
Keyword(s):  
Image Segmentation ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Local Optima ◽  
Fireworks Algorithm ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Unsupervised Image Segmentation

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

scholarly journals Image Segmentation Using the EM/MPM Algorithm

2021 ◽  
pp. 618-622
Author(s):  
Hanane DALIMI ◽  
Mohamed AFIFI ◽  
Said AMAR
Keyword(s):  
Image Segmentation ◽  
Estimation Method ◽  
Segmentation Method ◽  
Histogram Method ◽  
Markov Field ◽  
Unsupervised Image Segmentation

In this article we propose to place our work in a Markovian framework for unsupervised image segmentation. We give one of the procedures for estimating the parameters of a Markov field, we limit the work to the EM estimation method and the Posterior Marginal Maximization (MPM) segmentation method. Estimating the number of regions who compones the image is relatively difficult, we try to solve this problem by the K-means Histogram method.

scholarly journals An Ore Image Segmentation Method Based on RDU-Net Model

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4979
Author(s):  
Dong Xiao ◽  
Xiwen Liu ◽  
Ba Tuan Le ◽  
Zhiwen Ji ◽  
Xiaoyu Sun
Keyword(s):  
Image Segmentation ◽  
Production Efficiency ◽  
Fragment Size ◽  
Conveyor Belt ◽  
Segmentation Method ◽  
Residual Structure ◽  
Production Safety ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Main Index

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

scholarly journals TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodong Huang ◽  
Hui Zhang ◽  
Li Zhuo ◽  
Xiaoguang Li ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Positional Information ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmentation Methods ◽  
Feature Pyramid

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

scholarly journals A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuqing Yang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods ◽  
Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

scholarly journals A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Diana Baby ◽  
Sujitha Juliet Devaraj ◽  
Soumya Mathew ◽  
M. M. Anishin Raj ◽  
B. Karthikeyan
Keyword(s):  
Image Segmentation ◽  
Performance Comparison ◽  
Segmentation Methods ◽  
Unsupervised Image Segmentation ◽  
A Performance

Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Basu Dev Shivahare ◽  
S.K. Gupta
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Objective Evaluation ◽  
Whale Optimization Algorithm ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Whale Optimization ◽  
Novel Variant ◽  
Multiple Regions ◽  
Multi Level

Abstract: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is challenging problem. This paper introduces a novel multi-level thresholding based image segmentation method. The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analysis the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods. The segmentation results are validated in terms of subjective and objective evaluation. Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods

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