Chung-Yang Hsieh

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In data mining and pattern classification, feature extraction and representation methods are a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature, numbers of novel feature extraction and representation methods have been proposed. However, many of them only focus on specific(More)
In statistics, Canonical Correlation Analysis (CCA) is a kind of method to effectively analyze the correlation between two data. In recent years, some methods reported in literatures treated the action video sequences as the tensors, and calculated the similarity between two video sequences using CCA. Each of these tensors was unfolded into several matrices(More)
In this paper, we present a novel approach for human action and gesture recognition using dual-complementary tensors. In particular, the proposed method constructs a compact and yet discriminative representation by normalizing the input video volume into dual tensors. One tensor is obtained from the raw video volume data and the other one is obtained from(More)
In this paper, we propose a novel framework for video-based human action recognition, which can effectively resolve the difficulty caused by large variations within each action category. We first use the cloud of interest points to represent human action, due to its effectiveness in extracting spatiotemporal information necessary to reliability distinguish(More)
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