Alignment of nearly-repetitive contents in a video stream with manifold embedding

This paper presents an approach to identifying nearly repetitive contents in a stream of video where prior information such as the number, the length and contents of repetitions are not known. The approach is novel in that it does not require a template for searching or learning repeated contents. Instead it analyses a video by characterising the spatial and temporal information embedded in a frame sequence. A video is represented with its spatio-temporal features, which are analysed in the embedded manifold to reconstruct the underlying structure so that repeated contents can be reorganised. The approach is evaluated using rushes videos, where numerous repetitions are found. The experiments show that overall performance is improved using the extension of manifold learning with the spatio-temporal representation.