The increasing amount of digital audio-visual content accessible today calls for scalable solutions for content based search. The state of the art solutions reveal linear scalability in respect to the collection size due to the large number of distance computations needed for comparing low level audio-visual features. As a result, search in large audio-visual collections is limited to associate...

Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. This metric has already been successfully exploited, altogether, for defining unbiased clustering quality indexes, for efficient cluster labeling, as well as for substituting to distance in the clustering process, like in the IGNGF incremental clustering...

In this paper, we define the concept of compatible and weakly compatible mappings in b-metric spaces and inspired by the Ciric and et. al method, we produce appropriate conditions for given the unique common fixed point for a family of the even number of self-maps with together another two self-maps in a complete b-metric spaces. Also, we generalize this common fixed point theorem for a seq...

We formulate the problem of metric learning for k nearest neighbor classificationas a large margin structured prediction problem, with a latent variable representingthe choice of neighbors and the task loss directly corresponding to classificationerror. We describe an efficient algorithm for exact loss augmented inference, anda fast gradient descent algorithm for learning in thi...

An example of a D-metric space is given, in which D-metric convergence does not define a topology and in which a convergent sequence can have infinitely many limits. Certain methods for constructing D-metric spaces from a given metric space are developed and are used in constructing (1) an example of a D-metric space in which D-metric convergence defines a topology which is T1 but not Hausdorff...

This paper suggests two novel ideas to develop new proximal variable-metric methods for solving a class of composite convex optimization problems. The first idea is utilize parameterization strategy the optimality condition design homotopy algorithms that can achieve linear convergence and finite global iteration-complexity bounds. We identify at least three subclasses problems in which our app...