Multimedia Big Data Analysis Framework For Semantic...

%chapter{Conclusions and Future Work} chapter{CONCLUSIONS AND FUTURE WORK} label{chapter:Conclusions} section{Conclusions} In this dissertation a multimedia big data analysis framework for semantic information management and retrieval is presented. It contains three coherent components, namely multimedia semantic representation, multimedia concept classification and summarization, and multimedia temporal semantics analysis and ensemble learning. These three components are seamlessly integrated and act as a coherent entity to provide essential functionalities in the proposed information management and retrieval framework. More specifically: egin{itemize} item A novel correlation-based feature analysis method is presented to derive HCFGs for multimedia semantic retrieval on mobile devices. The proposed framework explores the mutual information from multiple modalities by performing correlation analysis for each feature pair and separating the original feature set into different HCFGs by using the affinity propagation algorithm at the feature level. Then, a novel fusion scheme is proposed to fuse the testing scores from selected HCFGs to obtain optimal performance. Finally, an iPad application is developed based on our proposed system with a user-feedback processing system to refine the retrieval results. item A hierarchical disaster image classification scheme based on textual and visual information fusion is proposed for enhancing disaster situation reports withShow MoreRelatedMultimedia Concept Detection For A Big Data Environment5682 Words   |  23 Pages Multimedia concept detection is a challenging topic due to the well known class imbalance issue especially in a big data environment. 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