Self-Supervised Learning for Robust Video Indexing

The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approaches often make use of thresholding techniques or supervised learning which requires labeling of possibly large training sets. Furthermore, the application of the same training model or parameters might lead to a suboptimal indexing accuracy for a given video. In this paper, we propose to use a novel self-supervised learning framework for robust video indexing to address this issue. Based on an initial classification result for a given video, the best features are selected by adaboost and are then used to train SVM (support vector machine) classifiers, all on the given video. Finally, a specialized ensemble of classifiers is employed for the given video for decision making. Experimental results show that a state-of-the-art video cut detection approach can be significantly improved by the self-supervised learning approach