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Advanced Model for Human Action Annotation Based on Background Subtraction Using Learning Vector Quantitation with Co-occurrence Matrix Features

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This paper presents an advanced model for human action annotation. The proposed technique is splitting human objects in two parts, upper and under part of human beings. From these two parts, a model to extract the feature vectors by GLCM was proposed as feature for classification.  The first method is to extract the Haralick features out of GLCM and the next step is the normalization process for converting co-occurrence feature matrix into various vectors as feature for classification. The research employs learning vector quantification to classify all feature vectors. Finally, the experiment conducted by utilizing Weizmann dataset shows that this approach method achieves an accuracy of 84.7%.
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Annotation; GLCM; Classification; Learning Vector Quantification

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