LAUSR

The semantic video indexing problem is still underexplored. Solutions to the problem will significantly enrich the experience of video search, monitoring, and surveillance. This paper concerns scene detection and annotation, and specifically, the task of video structure mining for video indexing using deep features. The paper proposes and implements a pipeline that consists of feature extraction and filtering, shot clustering, and labeling stages. A deep convolutional network is used as the source of the features. The pipeline is evaluated using metrics for both scene detection and annotation. The results obtained show high scene detection and annotation quality estimated with various metrics. Additionally, we performed an overview and analysis of contemporary segmentation and annotation metrics. The outcome of this work can be applied to semantic video annotation in real time.