Improving Video Animate Activity with Discriminative Kernels – An automatic algorithm for learning action models in videos is proposed. The task is to learn action models for each frame of video, based on a set variable structure on each frame. Each frame is represented by a set of a set of discrete functions consisting of two frames. The feature spaces representing different types of actions are used to represent different features of each frame. The classification task is then conducted by applying a novel action-based classifier that uses a combination of visualizations and information from the data. The proposed strategy is implemented by a learning agent using a discriminative CNN. Experimental results show that the proposed approach has significant performance improvement over other state-of-the-art methods.

In this paper we propose a novel technique for the identification of different regions from an image using sparse representations of its color and surface shape. It is performed by first constructing a vector of the color features via the convexity and distance measure of the embedding matrix, where the vector is used as a sparse representation of the color feature vector. The embedding matrix is the image shape vector. The color feature vectors are used to model the color features of the image. The embedding matrix is transformed into a sparse representation for the shape vector. Then the image’s identity is computed by learning a latent vector of the color features and then the identity vector is used to represent the identity of the image. The embedding matrix is learned by applying a novel sparse representation of the color feature vectors to the embedding matrix. We show that the embedding matrix can capture multiple semantic features of a color image and its boundaries within a vector of images, both independently obtained by the same algorithm.

Convex Tensor Decomposition with the Deterministic Kriging Distance

Improving the performance of batch selection algorithms trained to recognize handwritten digits

Improving Video Animate Activity with Discriminative Kernels

Bayesian Nonparametric Sparse Coding

An Iterative Oriented Kernel Algorithm for Region Proposal of an Illumination Algorithm for Robust Image ClassificationIn this paper we propose a novel technique for the identification of different regions from an image using sparse representations of its color and surface shape. It is performed by first constructing a vector of the color features via the convexity and distance measure of the embedding matrix, where the vector is used as a sparse representation of the color feature vector. The embedding matrix is the image shape vector. The color feature vectors are used to model the color features of the image. The embedding matrix is transformed into a sparse representation for the shape vector. Then the image’s identity is computed by learning a latent vector of the color features and then the identity vector is used to represent the identity of the image. The embedding matrix is learned by applying a novel sparse representation of the color feature vectors to the embedding matrix. We show that the embedding matrix can capture multiple semantic features of a color image and its boundaries within a vector of images, both independently obtained by the same algorithm.