Feature selection with equalized salience measures and its application to segmentation

Segmentation is a crucial step in computer vision in which texture plays an important role. The existence of a large amount of methods from which texture can be computed is, sometimes, a hurdle to overcome when it comes to modeling solutions for texture-based segmentation. Following the excellence of the natural vision system and its generality, this work has adopted a feature selection method based on salience of synaptic connections of a Multilayer Perceptron neural network. Unlike traditional approaches, this paper introduces an equalization scheme to salience measures which contributed to significantly improve the selection of the most suitable features and, hence, yield better segmentation. The proposed method is compared with exhaustive search according to the Jeffrey-Matusita distance criterion. Segmentation for images of natural scenes has also been provided as a probable application of the method.