Image Segmentation Using Stake-Denotes Algorithm
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 8
Abstract
In this paper, we present stake k-mean fragmentation and regions tracking model, which aims at combining color, texture, pattern, and motion features. in the first the stake algorithm segmented the objects which are tracking and realized them ;second the global motion of the video sequence is estimated and compensated with presenting algorithms. The spatio-temporal map is updated and compensated using stake fragmentation model to keep consistency in video objects tracking. The Stake algorithm considers the stakes’ placement which should be located as far as possible from each other to withstand against the pressure distribution of a roof, as identical to the number of centroidsamongst the data distribution. This algorithm is able to optimize the K-means clustering for icon fragmentation in aspects of precision and computation time. It designates the initial cancroids’ positions by calculating the accumulated distance metric between each data point and all previous cancroids, and then selects data points which have the maximum distance as new initial cancroids. This project presents a new approach for icon fragmentation by applying Stake-Kmeans algorithm. This fragmentation process includes a new mechanism for clustering the elements of highresolution icons in order to improve precision and reduce computation time. The system applies K-means clustering to the icon fragmentation after optimized by Stake Algorithm. This algorithm distributes all initial centroids according to the maximum accumulated distance metric.
Authors and Affiliations
Manohar Bammidi, V. Jeevan Kumar
Keywords
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