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Publikováno v:
WEIT
Data clustering is an important task in data mining, image processing and other pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). The performance of the FCM is strongly affected by the selection o
Publikováno v:
ICIP
Conventional fuzzy C-means (FCM) algorithm does not consider spatial information in the clustering, which makes it sensitive to noise and inefficient. In order to overcome these problems, we propose a fast anti-noise FCM algorithm for image segmentat
Publikováno v:
IFSA/NAFIPS
Nowadays the bag-of-visual-words is a very popular approach to perform the task of Visual Object Classification (VOC). Two key phases of VOC are the vocabulary building step, i.e. the construction of a `visual dictionary' including common codewords i
Autor:
Songhua Xie, Hui Nie
Publikováno v:
2013 Third International Conference on Intelligent System Design and Engineering Applications.
Those retinal vascular image without regular background and fixed contrast, make conventional approaches hard to achieve a satisfactory partition. So this paper presents a novel segmentation algorithm -- combination of genetic algorithms and FCM fuzz
Publikováno v:
2012 International Conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM).
Digital mammograms are difficult images to interpret. Data clustering plays a very crucial role in automatic detection of clustered calcifications in digital mammograms. The aim of this paper is to review and compare the performance of the three main
Autor:
Samir Bara, Mounir Ait Kerroum, Mohamed Ouadou, Nour-eddine El harchaoui, Driss Aboutajdine, Ahmed Hammouch
Publikováno v:
2012 IEEE International Conference on Complex Systems (ICCS).
Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is
Autor:
Richard Bowden, Ashish Gupta
Publikováno v:
ICIP
This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. Visual word ambiguity has recently been successfully modeled by kernel codebooks to provide improvement in classification performance over the s
Publikováno v:
ICMLC
In this paper, we propose a double weighted fuzzy clustering method for color image segmentation. In order to improve the performance of image segmentation by FCM algorithm, we use the window-based point density weighted method to calculate the membe
Publikováno v:
2011 International Conference on Multimedia, Signal Processing and Communication Technologies.
Image segmentation by level set method greatly depends on appropriate initialization and optimal configuration of the contour controlling parameters. Here in this paper a novel, robust image segmentation based on fuzzy level set is been presented. Sp
Publikováno v:
CIS
FCM algorithm is apt to fall into the local optimization, and what fast FCM algorithm can find optimum is greatly depended on the initialization. PSO-based FCM clustering algorithm avoids the local optima, and also is robust to initialization. The fl