A system for image analysis and classification based on a knowledge structuring technique is presented. The knowledge structuring technique automatically creates a relevance map from salient areas 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 knowledge structuring technique is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure 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.
['Le Dong', 'Ebroul Izquierdo']
A Knowledge Structuring Technique for Image Classification