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CORRELATION-BASED FEATURE ANALYSIS AND MULTI-MODALITY FUSION FRAMEWORK FOR MULTIMEDIA SEMANTIC RETRIEVAL
| Citations: | 1 - 1 self |
BibTeX
@MISC{Ha_correlation-basedfeature,
author = {Hsin-yu Ha and Yimin Yang and Fausto C. Fleites and Shu-ching Chen},
title = {CORRELATION-BASED FEATURE ANALYSIS AND MULTI-MODALITY FUSION FRAMEWORK FOR MULTIMEDIA SEMANTIC RETRIEVAL},
year = {}
}
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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. Index Terms — multimedia semantic retrieval, feature correlation, maximum spanning tree, multi-modality, fusion 1.
Keyphrases
feature space feature group correlation matrix final retrieval cfa method feature correlation multimedia semantic concept retrieval generated score different feature group maximum spanning tree semantic retrieval multi-modality fusion index term intra-group correlation cfa-mmf framework inter-group correlation graph cut procedure feature pair correlation different classifier hidden coherent feature group feature analysis nuswide-lite data set experimental result
