TV News Commercials Detection using Success based Locally Weighted Kernel Combination

TL;DR

This paper introduces a multiple kernel learning approach with success-based weighting for detecting commercials in TV news videos, demonstrating improved accuracy over baseline methods on multiple datasets.

Contribution

It proposes a novel success-based kernel weighting scheme using support vector regression and introduces a new TV commercials dataset for benchmarking.

Findings

Outperformed baseline methods on 6 of 8 datasets

Effective kernel combination improves commercial detection accuracy

New TV commercials dataset of 150 hours created

Abstract

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a intermediate fusion approach where, a SVM is trained with a weighted linear combination of different kernel functions instead of single kernel function. Each kernel function is characterized by a feature set and kernel type. We identify the feature sub-space locations of the prediction success of a particular classifier trained only with particular kernel function. We propose to estimate a weighing function using support vector regression (with RBF kernel) for each kernel function which has high values (near 1.0) where the classifier learned on kernel function succeeded and lower values (nearly 0.0) otherwise. Second contribution of this work is TV News Commercials Dataset of 150 Hours of News videos. Classifier trained with our proposed scheme has outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV commercials dataset.