Abstract
This paper presents a method for the classification of animated video that does not rely on hue or saturation information, and aims to achieve a high level of performance in the context of automatic video categorization and encoder parameterization for low bit rate video processing. While existing animated and genre-based video classification approaches have achieved good results, they are highly dependent on color extraction and thus cannot be used to accurately analyze grayscale video content and realistic animation styles, such as pixilation, where the color palette is not sufficiently distinct from live action content. We first introduce a dataset specifically designed to test animation video classification with a high variety of visual content. We then present a set of hue-independent features based on spatial and temporal characteristics of animation video, which are used to perform the classification task. Most of these features have not previously been utilized in existing animation video classification approaches. Finally, an overview of test results, representing a success rate of over 80%, proves the viability of the proposed classification method.
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