Hsin-Yu Ha

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In this paper, we propose an extended deep learning approach that incorporates instance selection and bootstrapping techniques for imbalanced data classification. In supervised learning, classification performance often deteriorates when the training set is imbalanced where at least one of the classes has a substantially fewer number of instances than the(More)
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)
—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)
The booming multimedia technology is incurring a thriving multi-media data propagation. As multimedia data have become more essential, taking over a major potion of the content processed by many applications, it is important to leverage data mining methods to associate the low-level features extracted from multimedia data to high-level semantic concepts. In(More)
Feature selection is an actively researched topic in varies domains, mainly owing to its ability in greatly reducing feature space and associated computational time. Given the explosive growth of high-dimensional multimedia data, a well-designed feature selection method can be leveraged in classifying multimedia contents into high-level semantic concepts.(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 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)
Nowadays, concept detection from multimedia data is considered as an emerging topic due to its applicability to various applications in both academia and industry. However, there are some inevitable challenges including the high volume and variety of multimedia data as well as its skewed distribution. To cope with these challenges, in this paper, a novel(More)