Bag-of-Genres for Video Genre Retrieval
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This paper presents a higher level representation for videos aiming at video genre retrieval. In video genre retrieval, there is a challenge that videos may comprise multiple categories, for instance, news videos may be composed of sports, documentary, and action. Therefore, it is interesting to encode the distribution of such genres in a compact and effective manner. We propose to create a visual dictionary using a genre classifier. Each visual word in the proposed model corresponds to a region in the classification space determined by the classifier's model learned on the training frames. Therefore, the video feature vector contains a summary of the activations of each genre in its contents. We evaluate the bag-of-genres model for video genre retrieval, using the dataset of MediaEval Tagging Task of 2012. Results show that the proposed model increases the quality of the representation being more compact than existing features.
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