Hypersphere topology creation for image classification

A kind of topology creation strategy for image analysis and classification is presented. The topology creation strategy automatically generates a relevance map from essential regions of natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of the topology creation strategy is a distribution mapping rule involving two basic modules: structured low-level feature extraction using convolution neural network and a topology creation module based on a hypersphere neural network. 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.