Multifeature analysis and semantic context learning for image classification

Abstract

This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this context model is used to infer object classes within images. Selected results from a comprehensive experimental evaluation are reported to show the effectiveness of the proposed approaches.