A topology synthesizing approach for classification of visual information
A system for classification of visual information based on a topology synthesizing approach is presented. The topology synthesizing approach automatically creates a relevance map from essential regions of visual information. It also derives a set of well-organized representations from low-level description to drive the final classification. The backbone of the topology synthesizing approach is a mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a self-organizing tree algorithm. Classification is achieved by simulating high-level top-down visual information perception and classifying using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.