Learning graph representation with Randomized Neural Network for dynamic texture classification

Graph Representation Learning and Graph Classification

Many real-world problems are represented by using graphs. For example, given a graph of a chemical compound, we want do determine whether it causes a gene mutation or not. As another example, given a graph of a social network, we want to predict a potential friendship that does not exist but it is likely to appear soon. Many of these questions can be answered by using machine learning methods i...

Learning classifier systems with neural network representation

Convolutional neural network on three orthogonal planes for dynamic texture classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the ne...

A combined neural network approach for texture classification

-In this article, we present a two-stage neural network structure that combines the characteristics o f selforganizing map ( S O M ) and multilayer perceptron (MLP) for the problem of texture classification. The texture features are extracted using a multichannel approach. The channels comprise o f a set o f Gabor filters having different sizes, orientations, and frequencies to constitute N-dim...

Learning of Graph Compressed Dictionaries for Sparse Representation Classification

Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured in operational environments, and over multiple cameras, to improve robustness to variations. This paper focuses on Sparse Representation Classification (SRC) techniques that are suitable for the design of still-to-video FR systems based on unde...