Supervised video genre classification using optimum-path forest
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Multimedia-content classification has been paramount in the last years, mainly because of the massive data accessed daily. Video based retrieval and recommendation systems have attracted a considerable attention, since it is a profitable feature for several online and offline markets. In this work, we deal with the problem of automatic video classification in different genres based on visual information by means of Optimum-Path Forest (OPF), which is a recently developed graph-based pattern recognition technique. The aforementioned classifier is compared against with some state-of-the-art supervised machine learning techniques, such as Support Vector Machines and Bayesian classifier, being its efficiency and effectiveness evaluated in a number of datasets and problems.