• Journal of the Korean Society for Library and Information Science
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• v.37 no.4
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• pp.178-198
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• 2003

Mutual information as an association measure, has been used for various purposes as well as for calculating term similarity. There we, however, some limits in mutual information. It tends to emphasize low frequency terms extremely because the marginal value of mutual information changes inversely to frequency of terms. To compensate for this limit this study suggests relative mutual information(RMI) coefficients which normalize mutual information, and examines their characteristics in some details. The RMI coefficients also improve effectiveness of global query expansion when they are adapted to three different collections.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.29-38
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• 2015

Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.