A massive images classification method based on MapReduce parallel fuzzy C-means clustering

2021 ◽  
pp. 1-13
Author(s):  
Jinping Hu ◽  
Qian Cheng ◽  
Zhicheng Wen
Keyword(s):  
Real Time ◽  
Gray Level ◽  
Processing Efficiency ◽  
Single Node ◽  
Real Time Processing ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Low Performance ◽  
Speedup Ratio ◽  
Occurrence Matrix

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

Implementation and Comparative Analysis of k-means and Fuzzy c-means Clustering Algorithms for Tasks Allocation in Distributed Real Time System

2019 ◽  
Vol 10 (2) ◽  
pp. 66-86 ◽  
Author(s):  
Harendra Kumar ◽  
Isha Tyagi
Keyword(s):  
Real Time ◽  
High Performance ◽  
Distributed Processing ◽  
Clustering Algorithms ◽  
Processing System ◽  
Real Time Systems ◽  
Real Time System ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Time Systems

Distributing tasks to processors in distributed real time systems is an important step for obtaining high performance. Scheduling algorithms play a vital role in achieving better performance and high throughput in heterogeneous distributed real time systems. To make the best use of the computational power available, it is essential to assign the tasks to the processor whose characteristics are most appropriate for the execution of the tasks in a distributed processing system. This study develops two algorithms for clustering the heavily-communicating tasks to reduce the inter-tasks communication costs by using k-means and fuzzy c-means clustering techniques respectively. In order to minimize the system cost and response time, an algorithm is developed for the proper allocation of formed clusters to the most suitable processor. The present algorithms are collated with problems in literature. The proposed algorithms are formulated and applied to numerous numerical examples to demonstrate their effectiveness.

The Research of Leather Image Segmentation Using Texture Analysis Techniques

2014 ◽  
Vol 1030-1032 ◽  
pp. 1846-1850
Author(s):  
Hong Chen
Keyword(s):  
Image Segmentation ◽  
Surface Texture ◽  
Surface Defects ◽  
Spatial Information ◽  
Texture Feature ◽  
Gray Level ◽  
Analysis Techniques ◽  
Fuzzy C Means ◽  
Morphological Approach ◽  
Occurrence Matrix

The leather productions are produced rapidly in people’s living, the productions’ quality is required stricter. Leather must be detected include leather plainness; leather surface defects and the density of leather before they are produced to be productions.. The most important aspect is the surface defects; the defects’ location, size and quantity should be confirmed. One of the most important steps of leather defects detection is leather image segmentation so as to extract leather defects. Gray level co-occurrence matrix is used to extract a lot of leather surface texture feature, the method of optimized Fuzzy C-means is used to segment leather image in the article. The optimized Fuzzy C-means add the spatial information; the precision of segmentation is improved. The image needs to be treated use morphological approach after it is segmented. As a result, the defective areas are separated from non-defective areas successfully.

Implementation of a modified Fuzzy C-Means clustering algorithm for real-time applications

2005 ◽  
Vol 29 (8-9) ◽  
pp. 375-380 ◽  
Author(s):  
Jesús Lázaro ◽  
Jagoba Arias ◽  
José L. Martín ◽  
Carlos Cuadrado ◽  
Armando Astarloa
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Real Time Applications

scholarly journals A Real-Time Entity Monitoring based on States and Scenarios

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Mario José Diván ◽  
María Laura Sánchez-Reynoso
Keyword(s):  
Real Time ◽  
Decision Criteria ◽  
Real Time Processing ◽  
Discrete Simulation ◽  
Online Processing ◽  
Measurement Framework ◽  
New Concepts ◽  
Current Scenario ◽  
The Times ◽  
Occurrence Matrix

Scenario: The current markets require online processing and analysis of data as soon as they arrive to make decisions or implement actions as soon as possible. PAbMM is a real-time processing architecture specialized in measurement projects, where the processing is guided by measurement metadata derived from a measurement framework through the project definition. Objective: To extend the measurement framework incorporating scenarios and entity states as a way to online interpret the indicator’s decision criteria according to scenarios and entity states, approaching their conditional likelihoods. Methodology: An extension based on entity and context states is proposed to implement scenarios and entity states. A memory structure based on the occurrence matrix is defined to approach the associated conditional likelihoods while the data are processed. A new hierarchical complimentary schema is introduced to foster the project definition interoperability considering the new concepts. An extension of the cincamipd library was carried forward to support the complementary schema. An application case is shown as a proof-of-concept. Results: A discrete simulation is introduced for describing the times and sizes associated with the new schema when the volume of the projects to update grow-up. The results of the discrete simulation are very promising, only 0.308 seconds were necessary for updating 1000 active projects. Conclusions: The simulation provides an applicability reference to analyse its convenience according to the project requirements. This allows implementing scenarios and entity states to increase the suitability between indicators and decision criteria according to the current scenario and entity state under analysis.

scholarly journals Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuehong Zhu ◽  
Ying Han
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Marine Environment ◽  
Clustering Algorithm ◽  
Environment Monitoring ◽  
Real Time Monitoring ◽  
Virtual Reality Technology ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Marine Environment Monitoring

In modern society, with the rapid increase of population and the serious shortage of resources, the marine environment has been destroyed; there are also many people who go out to sea without permission, regardless of the legal constraints, fishing. This kind of behavior leads the marine environment to get worse and worse, so the real-time monitoring of the marine environment is very necessary. The main article marine environment monitoring, virtual reality technology, and fuzzy C-means clustering algorithm combine to improve the efficiency of monitoring and processing power of the data information. Through the application of virtual reality technology in the marine environment monitoring base and real-time simulation of the dynamics of the ocean, the monitoring personnel can understand the emergencies on the sea in time; the fuzzy C-means clustering algorithm is applied to the server receiving the data to classify the data. It is found in the experiment that when virtual reality technology and fuzzy C-means clustering algorithm are not used, the data of marine environment monitoring takes more than 1.3 s to return to the server, but, after applying two advanced technologies, the return efficiency is greatly improved, and the time consumed is less than 0.82 s. The results show that virtual reality technology and fuzzy C-means clustering algorithm can improve the efficiency of environmental monitoring, and through virtual reality technology, real-time monitoring of the marine environment can be achieved; in the absence of people out to sea, the actual situation on the sea can be clearly understood; and fuzzy C-means clustering algorithm can improve the speed of data processing, so that the monitoring personnel can solve the problem faster.

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