Fausto Fleites

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In this paper, a hierarchical disaster image classification (HDIC) framework based on multi-source data fusion (MSDF) and multiple correspondence analysis (MCA) is proposed to aid emergency managers in disaster response situations. The HDIC framework classifies images into different disaster categories and sub-categories using a pre-defined semantic(More)
—Semantic concept detection is among the most important and challenging topics in multimedia research. Its objective is to effectively identify high-level semantic concepts from low-level features for multimedia data analysis and management. In this paper, a novel re-ranking method is proposed based on correlation among concepts to automatically refine(More)
—In this paper we propose a novel framework for object retrieval based on automatic foreground object extraction and multi-layer information integration. Specifically, user interested objects are firstly detected from unconstrained videos via a multimodal cues method; then an automatic object extraction algorithm based on GrabCut is applied to separate(More)
—We present a novel visual analytics system and multimedia enabled mobile application that allows emergency management (EM) personnel access to timely and relevant disaster situation information. The system is able to semantically integrate text-based emergency management disaster situation reports with related disaster imagery taken in the field by EM(More)
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),(More)
In this paper, the details about FIU-UM group TRECVID2009 high-level feature extraction task submission are presented. Six runs were conducted using different feature sets, data pruning approaches, classification algorithms, and ranking methods. A proportion of TRECVID2009 development data were randomly sampled from the whole development data archives (all(More)