1 research outputs found
Learning Local Complex Features using Randomized Neural Networks for Texture Analysis
Texture is a visual attribute largely used in many problems of image
analysis. Currently, many methods that use learning techniques have been
proposed for texture discrimination, achieving improved performance over
previous handcrafted methods. In this paper, we present a new approach that
combines a learning technique and the Complex Network (CN) theory for texture
analysis. This method takes advantage of the representation capacity of CN to
model a texture image as a directed network and uses the topological
information of vertices to train a randomized neural network. This neural
network has a single hidden layer and uses a fast learning algorithm, which is
able to learn local CN patterns for texture characterization. Thus, we use the
weighs of the trained neural network to compose a feature vector. These feature
vectors are evaluated in a classification experiment in four widely used image
databases. Experimental results show a high classification performance of the
proposed method when compared to other methods, indicating that our approach
can be used in many image analysis problems