Abstract
In this paper, we propose a new representation of images. We called that representation as “Dominant Colors”. We defined the dissimilarity of two images as a vector contains the difference in order of each dominant color between the two images representations. Our new image representation and dissimilarity measure are utilized to detect the abrupt cuts in news videos. A neural network trained with our new dissimilarity measure to classify between two classes of news videos frames: cut frames, and non-cut frames. Our proposed system tested in real news videos from different TV channels. Experimental results show the effectiveness of our new image representation and dissimilarity measure to describe the images, and detect the abrupt cuts in news videos.
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Zedan, I.A., Elsayed, K.M., Emary, E. (2017). Abrupt Cut Detection in News Videos Using Dominant Colors Representation. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_31
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DOI: https://doi.org/10.1007/978-3-319-48308-5_31
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