Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Fundamenta Informaticae

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: point symmetry based distance

Sortuj według:

Ogranicz wyniki do:

Semi-GAPS: A Semi-supervised Clustering Method Using Point Symmetry

Saha S., Bandyopadhyay S.

Fundamenta Informaticae

2009

Vol. 96, nr 1/2

195-209

In this paper, an evolutionary technique for the semi-supervised clustering is proposed. The proposed technique uses a point symmetry based distance measure. Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. In this paper the existing point symmetry based genetic clustering technique, GAPS-clustering, is extended in two different ways to handle the semi-supervised classification problem. The proposed semi-GAPS clustering algorithmis able to detect any type of clusters irrespective of shape, size and convexity as long as they possess the point symmetry property. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. Experimental results demonstrate practical performance benefits of the methodology in detecting classes having symmetrical shapes in case of semi-supervised clustering.

Fuzzy Symmetry Based Real-Coded Genetic Clustering Technique for Automatic Pixel Classification in Remote Sensing Imagery

Saha S., Bandyopadhyay S.

Fundamenta Informaticae

2008

Vol. 84, nr 3-4

471-492

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes presents a challenging task. In this paper, a newly developed real-coded variable string length genetic fuzzy clustering technique with a new point symmetry distance is used for this purpose. The proposed algorithm is capable of automatically determining the number of segments present in an image. Here assignment of pixels to different clusters is done based on the point symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, and a newly developed fuzzy point symmetry distance based cluster validity index, FSym-index, is used as a measure of the validity of the corresponding partition. This validity index is able to correctly indicate presence of clusters of different sizes and shapes as long as they are internally symmetrical. The space and time complexities of the proposed algorithm are also derived. The effectiveness of the proposed technique is first demonstrated in identifying two small objects from a large background from an artificially generated image and then in identifying different landcover regions in remote sensing imagery. Results are compared with those obtained using the well known fuzzy C-means algorithm both qualitatively and quantitatively.