Content Based Video Categorization Using Relational Clustering with Local Scale Parameter

This paper introduces a novel approach for efficient video categorization. It relies on two main components. The first one is a new relational clustering technique that identifies video key frames by learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined with respect to each cluster. We minimize one objective function to optimize the optimal partition and the cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the categorization task takes advantages of the local learned kernel. The second component of the proposed video categorization system consists in discovering the video categories in an unsupervised manner using the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high dimensional real data. Also, we assess the proposed video categorization system using a real video collection and LSL algorithm.