Hyperspectral sensors, by accurate sampling of object reflectance into numerous narrow spectral bands, can provide valuable information to identify different landcover classes. Nevertheless, classification of these data has some problems. In particular, one of the most well-known of them is not having adequate training data for learning of classifiers. One possible solution to this problem is t...

A new method for image segmentation is proposed in this paper, which combines the watershed transform, KFCM and level set method. The watershed transform is first used to presegment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The kernel fuzzy c-means (KFCM) was used to generate an initial contour curve whic...

Kernel fuzzy C-mean clustering (KFCM) algorithm is effective for high-dimensional data, but this algorithm has some defects of sensitivity to initialization and local optima. Artificial Bee Colony (ABC) algorithm is based on intelligent behaviors of honey bee swarm. It has the properties of strong global optimization and fast convergence speed. A KFCM algorithm based on improved ABC is proposed...

Color image segmentation plays an important role in computer vision and image processing applications. Kernel-based fuzzy C-means (KFCM) is well known and powerful methods used in image segmentation. Moreover, an appropriate assigning weight to features can improve its performance. This paper focuses on improving the image segmentation capabilities of KFCM based on feature weighting. It employs...

The size of everyday data sets is outpacing the capability of computational hardware to analyze these data sets. Social networking and mobile computing alone are producing data sets that are growing by terabytes every day. Because these data often cannot be loaded into a computer’s working memory, most literal algorithms (algorithms that require access to the full data set) cannot be used. One ...

This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid geneticArtificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and ge...

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy CMeans(FCM) algorithm, Kernel Fuzzy CMeans(KFCM), Intuitionistic Kernelized Fuzzy CMeans(KIFCM), Kernelized Type-II Fuzzy CMeans(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qual...

-Clustering algorithms are an integral part of both computational intelligence and pattern recognition. It is unsupervised methods for classifying data into subgroups with similarity based inter cluster and intra cluster. In fuzzy clustering algorithms, mainly used algorithm is Fuzzy c-means (FCM) algorithm. This FCM algorithm is efficient only for spherical data when the input of the data stru...

Image segmentation plays an important role in image analysis. It is one of the first and most important tasks in image analysis and computer vision. This proposed system presents a variation of fuzzy cmeans algorithm that provides image clustering. Based on the Mercer kernel, the kernel fuzzy c-means clustering algorithm (KFCM) is derived from the fuzzy c-means clustering algorithm (FCM).The KF...