Correlation-based Feature Analysis and Multi-Modality Fusion framework for multimedia semantic retrieval

Abstract

In this paper, we propose a Correlation based Feature Analysis (CFA) and Multi-Modality Fusion (CFA-MMF) framework for multimedia semantic concept retrieval. The CFA method is able to reduce the feature space and capture the correlation between features, separating the feature set into different feature groups, called Hidden Coherent Feature Groups (HCFGs), based on Maximum Spanning Tree (MaxST) algorithm. A correlation matrix is built upon feature pair correlations, and then a MaxST is constructed based on the correlation matrix. By performing a graph cut procedure on the MaxST, a set of feature groups are obtained, where the intra-group correlation is maximized and the inter-group correlation is minimized. Finally, one classifier is trained for each of the feature groups, and the generated scores from different classifiers are fused for the final retrieval. The proposed framework is effective because it reduces the dimensionality of the feature space. The experimental results on the NUSWIDE-Lite data set demonstrate the effectiveness of the proposed CFA-MMF framework.