A Survey On Unsupervised Evaluation Criteria For Image Clustering Validation

2017 ◽  
Vol 2 (2) ◽  
pp. 83-92 ◽  
Author(s):  
Akar Taher
Keyword(s):  
Evaluation Criteria ◽  
Image Clustering ◽  
Clustering Validation

Variable Length PSO-Based Image Clustering for Image Denoising

2016 ◽  
pp. 294-320 ◽  
Author(s):  
Somnath Mukhopadhyay ◽  
J. K. Mandal ◽  
Tandra Pal
Keyword(s):  
Digital Images ◽  
Fitness Function ◽  
Evaluation Criteria ◽  
Variable Length ◽  
Image Clustering ◽  
Initial Population ◽  
Restoration Technique ◽  
Image Prior ◽  
Weighted Median ◽  
Filtering Technique

This chapter proposed a variable length Particle Swarm Optimization based image clustering technique for restoration of noises from digital images. Here in this two step noise restoration technique the noise free pixels are kept unchanged. The denoising technique uses 3 × 3 test window on the center pixel of the noisy image. Prior to detection and filtering, variable length PSO based image clustering has been done. The output of clustering determines the performance of the subsequent stages of the algorithm. For denoising weighted median filtering technique is proposed. Variable length particles are considered and randomly encoded for the initial population. The length of particles is changed by adding and/or deleting cluster centers present in the particles. Three evaluation criteria are used in the fitness function of the proposed algorithm. The performance of the proposed algorithm is compared with some similar algorithms existing in the literature on several standard digital images.

scholarly journals Performance Comparison Of Evolutionary Algorithms For Image Clustering

2014 ◽  
Vol XL-7 ◽  
pp. 71-74
Author(s):  
P. Civicioglu ◽  
U. H. Atasever ◽  
C. Ozkan ◽  
E. Besdok ◽  
A. E. Karkinli ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Data Clustering ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Convergence Time ◽  
Image Clustering ◽  
Clustering Techniques ◽  
Sensing Applications ◽  
Clustering Validation

Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.

Transformation of evaluation criteria of lands withdrawn from agricultural turnover due to radioactive contamination

2020 ◽  
pp. 23-27
Author(s):  
Olga Merzlova
Keyword(s):  
Economic Evaluation ◽  
Radioactive Contamination ◽  
Chernobyl Accident ◽  
Evaluation Criteria ◽  
Contaminated Land ◽  
Degradation Processes ◽  
Agricultural Use ◽  
Land Clearance

One of the measures to eliminate the consequences of the Chernobyl accident was the exclusion of highly contaminated land from agricultural use. Due to the positive dynamics of the radiation situation, the issue of land return becomes relevant. However, in the period of exclusion of these lands the land clearance degradation processes were developing. The second part of the article is devoted to the issue of economic evaluation of the expediency of land return and the mutual coordination of the results of separate stages of complex ecological and economic evaluation. The research was carried out in Mogilev branch Institute of radiology (Republic of Belarus).

Performance Evaluation Criteria for Heat Exchangers Based on First Law Analysis

2005 ◽  
Vol 12 (2) ◽  
pp. 121-158 ◽  
Author(s):  
M. Yilmaz ◽  
O. Comakli ◽  
S. Yapici ◽  
O. N. Sara
Keyword(s):  
Performance Evaluation ◽  
Heat Exchangers ◽  
Evaluation Criteria

Objective Video Quality Method Based on Mutual Information and Human Visual System

2012 ◽  
Vol 58 (2) ◽  
pp. 147-152
Author(s):  
Michal Mardiak ◽  
Jaroslav Polec
Keyword(s):  
Mutual Information ◽  
Visual System ◽  
Human Visual System ◽  
Quality Evaluation ◽  
Video Quality ◽  
Evaluation Criteria ◽  
Test Sequence ◽  
Quality Metric ◽  
Two Stages ◽  
Video Quality Metric

Objective Video Quality Method Based on Mutual Information and Human Visual SystemIn this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.

Sign in / Sign up

Export Citation Format

Share Document