TV Commercial Detection Using Success Based Locally Weighted Kernel Combination

Classification problems using multiple kernel learning (MKL) algorithms achieve superior performance on account of using a weighted combination of base kernels on feature sub-sets. Each of the base kernels are characterized by the similarity measures defined over the feature sub-sets. Existing works in MKL have mostly used fixed weights which are shown to be related to the overall discriminative capability of corresponding base kernels. We argue that this class discrimination ability of a kernel is a local phenomenon and thus, advocate the necessity of using instance dependent functions for weighing the kernels. We propose a new framework for learning such weighing functions linked to ability of kernels to discriminate in the local regions of the feature space. During training, we first identify the regions of success in the feature sub-spaces, where the base kernels have high likelihood of success. These regions are identified by evaluating the performance of support vector machines (SVM) trained using corresponding (single) base kernels. The weighing functions are then estimated by using support vector regression (SVR). The target for SVRs is set to 1.0 for the successfully classified patterns and to 0.0, otherwise. The second contribution of this work is the construction and public domain release of a commercial detection dataset of 150 hours, acquired from 5 different TV news channels. Empirical results on 8 standard datasets and our own TV commercial detection dataset have shown the superiority of the proposed scheme of multiple kernel learning.