Data-intensive computing this approach is considered suitable for data-intensive computing and problems which are “embarrassingly parallel”, i.e. where it is relatively easy to separate the problem into a number of parallel tasks and there is no dependency or communication required between the tasks other than overall management of the tasks. These types of data processing problems are inherently adaptable to various forms of distributed computing including clusters, data grids, and cloud computing. Several common characteristics of data-intensive computing systems distinguish them from other forms of computing: A variety of system architectures have been implemented for data-intensive computing and large-scale data analysis applications
Feature (computer vision) point, where the vector is constructed from several different features extracted from the image data. During a learning phase, the network can itself find which combinations of different features are useful for solving the problem at hand. The extraction of features are sometimes made over several scalings. One of these methods is Scale-invariant feature transform (SIFT); in this algorithm, various scales of an image are analyzed to extract features. Feature (computer vision) In computer vision and image processing, a feature is a piece of information which is relevant for solving the computational task related to a certain application. This is