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Entropy based fuzzy classification of images on quality assessment

Indrajit De, Jaya Sil
2012 Journal of King Saud University: Computer and Information Sciences  
The paper aims at developing a fuzzy based no-reference image quality assessment system by utilizing human perception and entropy of images.  ...  Finally, fuzzy relational classifier (FRC) has been built using MOS based weight matrix and fuzzy partition matrix to establish correlation between features and class labels.  ...  MOS entropy based weight matrix Utilizing human perception about the visual quality of the images, MOS entropies are computed and classified using Algorithm 2 and the following equation: w iq ¼ E xi for  ... 
doi:10.1016/j.jksuci.2012.05.001 fatcat:bakemqlxlbahjbjf4sbfarwalq

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

M. H. Fazel Zarandi, A. Khadangi, F. Karimi, I. B. Turksen
2016 Journal of digital imaging  
The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing  ...  Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques.  ...  The number of clusters chosen by expert was validated by Kwon validity index, and the obtained result was C = 4 (C stands for number of clusters) [33] .  ... 
doi:10.1007/s10278-016-9884-y pmid:27198133 pmcid:PMC5114228 fatcat:nucotzdoozhtpdszlklpv7t22e

Colour image enhancement based on wavelet approximation coefficient histogram

K. Baranitharan, S. Srinivasa Rao Madane
2013 2013 International Conference on Current Trends in Engineering and Technology (ICCTET)  
Then clustering is taken place for lower band coefficient using K-means clustering (KMC) where number of cluster (K) is predicted by hill climbing algorithm.  ...  The performance is evaluated by Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM) index, Universal Quality Index (UQI) and Entropy.  ...  for image the colour images for human vision and to provide better contrast enhancement [8] .  ... 
doi:10.1109/icctet.2013.6675953 fatcat:qld263rzujgqtf5xg6wamtnlse

A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering

E. A. Zanaty
2013 American Journal of Remote Sensing  
In this paper, we introduce a new validity index method based on multi-degree entropy algorithm for determining the number of clusters automatically.  ...  Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators.  ...  Other approaches were used to incorporating kernels into fuzzy clustering algorithms for enhancing clustering algorithms designed to handle different shape clusters [8] .  ... 
doi:10.11648/j.ajrs.20130102.14 fatcat:usnbmdjytzda7dv6ydxqoq33a4

Unsupervised Partitioning of Numerical Attributes Using Fuzzy Sets

Bogdan Popescu, Andreea Popescu, Marius Brezovan, Eugen Ganea
2012 Conference on Computer Science and Information Systems  
The final goal is to use it for future decision making in automatic image annotation. Fuzzy Sets theory has been used as a base for our clustering algorithm and partitioning.  ...  We have planned this partitioning phase as an initial step in a more complex algorithm to be further studied and implemented.  ...  A cluster validity index for crisp (non fuzzy) clustering is proposed in [13] . An alternative has been proposed in [12] .  ... 
dblp:conf/fedcsis/PopescuPBG12 fatcat:uqm5b7pnkjfctk4fqzz7ayvwbq

A New Look at Public Services Inequality: The Consistency of Neighborhood Context and Citizens' Perception across Multiple Scales

2017 ISPRS International Journal of Geo-Information  
Neighborhood context and citizens' perception of place should be integrated to investigate urban segregation, thereby providing insights into the underlying societal inequality phenomenon and quality of  ...  A spatial consistency between objective neighborhood context and subjective individual perception of place plays a crucial role in propagating mixed-methods approaches (qualitative-quantitative) and improves  ...  (Fuzzy = Fuzzy clustering, HAC = Hierarchical clustering, Kmeans = K-means clustering, Mcluster = Gaussian Mixture Modelling for Model-Based Clustering, SKATER = Spatial "K"luster Analysis by Tree Edge  ... 
doi:10.3390/ijgi6070200 fatcat:tixmquf4pjhz7ks2olnu3rzetq

Super-Pixels Generation based on Fuzzy Similarity
퍼지 유사성 기반 슈퍼-픽셀 생성

Yong-Gil Kim, Kyung-ll Moon
2017 The Journal of The Institute of Internet Broadcasting and Communication  
For instance, human segmented images can be used as prototypes of good segmentations. In this paper, we combined SLIC algorithm with fuzzy clustering.  ...  Further, cluster validity has been used to evaluate the fitness of partitions produced by clustering algorithms.  ... 
doi:10.7236/jiibc.2017.17.2.147 fatcat:r3jie4fdfveejm75mak7haggry

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

Ramin Afshoon, Ali Harounabadi, Javad Mir Abedini
2014 International Journal of Computer Applications  
Human behavior, namely the objective perspective, is the essential source to obtain human thinking and real doings. For this reason, data mining approaches are used to acquire the objective source.  ...  In this research, proposed subtractive clustering is applied in evaluating educational web sites from the fuzzy objective perspective.  ...  The comparison is based on validity measurement of their clustering results. The number of clusters is changed for the fuzzy c-mean algorithm. The validity results are calculated for several cases.  ... 
doi:10.5120/17175-7264 fatcat:rsswv57edjewbph4r4qhts7xvu

Analysis of entrepreneur mental model and construction of its portrait

Yongzhong Zhang, Yonghui Dai, Haijian Chen
2021 Computer Science and Information Systems  
Since then, the methods of entrepreneur mental portrait are introduced, which including cluster analysis method and fuzzy comprehensive evaluation method.  ...  Firstly, according to existing research results, this paper summarizes three key factors that affect entrepreneurial mental model: prior knowledge, personality characteristics and opportunity perception  ...  After years of development of clustering analysis, the current clustering analysis methods have formed many algorithms, such as k-means algorithm, Clara algorithm, PCM fuzzy clustering algorithm, SOM self-organizing  ... 
doi:10.2298/csis210119023z fatcat:ycm3up2utbbalp7arrhm22jleq

Adaptive Foreground Object Extraction in Real Time Videos Using Fuzzy C Means with Weber Principle

M. Sivagami, T. Revathi, L. Jeganathan
2016 Indian Journal of Science and Technology  
Methods: The proposed foreground extraction technique models the background using cluster centroids and optimized using fuzzy-c-means technique.  ...  Improvement: This proposed real-time foreground extraction approach yields better results than the previous algorithms with respect to quality of extraction and memory consumption.  ...  The clustering validity indices are i) Fuzziness in partition matrix U ii) Fukuyama-sugeno index iii) Xie-Beni index Fuzziness in Partition Matrix U 2 1 ( ) 1 1 1 c M U I ik M i k µ   =   ∑ ∑    ... 
doi:10.17485/ijst/2016/v9i29/80607 fatcat:m5hprccy6nf4roti7bzetlnf44

Clustering Indian stock market data for portfolio management

S.R. Nanda, B. Mahanty, M.K. Tiwari
2010 Expert systems with applications  
Results of our analysis show that Kmeans cluster analysis builds the most compact clusters as compared to SOM and Fuzzy C-means for stock classification data.  ...  We then select stocks from the clusters to build a portfolio, minimizing portfolio risk and compare the returns with that of the benchmark index, i.e. Sensex.  ...  The Partition index and the Xie and Beni's index shows that optimal number of clusters could be 11 for Fuzzy C-means clustering.  ... 
doi:10.1016/j.eswa.2010.06.026 fatcat:xupe7zetuvfszcfripoqdsgyhm

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

Yiping Li, Xiangbing Zhou, Jiangang Gu, Ke Guo, Wu Deng
2022 Applied Sciences  
To mitigate the problem, a hybrid heuristic "fuzzy system-particle swarm-genetic" algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm  ...  The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI).  ...  • The PSO algorithm, fuzzy system algorithm, and genetic algorithm were organically combined to obtain the optimal initial clustering centers. • Three unsupervised evaluation indexes (SP, SC and SSE  ... 
doi:10.3390/app12168047 fatcat:bzbqcgf6rjelnpjzqfpi75nhmm

INTELLIGENT SEGMENTATION OF FRUIT IMAGES USING AN INTEGRATED THRESHOLDING AND ADAPTIVE K-MEANS METHOD (TSNKM)

Hamirul 'Aini Hambali, Sharifah Lailee Syed Abdullah, Nursuriati Jamil, Hazaruddin Harun
2016 Jurnal Teknologi  
The developed algorithm is an integration of modified thresholding and adaptive K-means method.  ...  Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques.  ...  Acknowledgement The authors would like to thank for the support given to this research by Ministry of Higher Education for the financial support under 600-RMI/ERGS 5/3 Exploratory Research Grant Scheme  ... 
doi:10.11113/jt.v78.8993 fatcat:ujg5xzg3vzey3oowoh4fujgil4

Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

Serkan Kiranyaz, Stefan Uhlmann (EURASIP Member), Turker Ince, Moncef Gabbouj
2010 EURASIP Journal on Advances in Signal Processing  
color space, distance metric and a proper validity index function.  ...  Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment.  ...  Each category then has a wealth of subcategories and different algorithmic approaches for finding the clusters. Clustering can also be performed in two different modes: hard (or crisp) and fuzzy.  ... 
doi:10.1155/2009/451638 fatcat:qwkyipwwobezdaccb7volqxtge

Tumor Region detection in MRI Images using Novel Kernalized Fuzzy C-Means Clustering Algorithm

P.Gangadhara Reddy, T. Ramashri, K.Lokesh Krishna
2022 Journal of scientific research  
In this paper, we suggest an algorithm for identification of human brain tumors in Magnetic Resonance Imaging (MRI) that contains a Kernel Fuzzy C-Means (KFCM).  ...  Secondly, updated membership will be calculated using a fuzzy Cmeans (FCM) Algorithm and, final, the kernel FCM clustering algorithm is used to detect the tumors location by updating its membership function  ...  We only look for the best index for the stable kernelized fuzzy C-mean clustering on the GRBF kernel.  ... 
doi:10.37398/jsr.2022.660328 fatcat:fiebl2zfzfgw5lhmnj4md7ghsa
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