The Thin Plate Spline warping based Image Morphing algorithm is the best choice.
Shenbagalakshmi Gunasekaran, I. Vasudevan · International Journal of Computer Applications Technology and Research · 2014
In clustering process, semi-supervised learning is a tutorial of contrivance learning methods that make usage of both labeled and unlabeled data for training -characteristically a trifling quantity of labeled data with a great quantity of unlabeled data.Semi-supervised learning cascades in the middle of unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data).Feature selection encompasses pinpointing a subsection of the most beneficial features that yields well-suited results as the inventive entire set of features.A feature selection algorithm may be appraised from both the good organization and usefulness points of view.Although the good organization concerns the time necessary to discover a subsection of features, the usefulness is related to the excellence of the subsection of features.Traditional methodologies for clustering data are based on metric resemblances, i.e., non-negative, symmetric, and satisfying the triangle unfairness measures using graph-based algorithm to replace this process in this project using more recent approaches, like Affinity Propagation (AP) algorithm can take as input also general non metric similarities.