Image Classification Based on Color Topic Model

Abstract

This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evaluations the COREL database demonstrate its promise of the effectiveness.