Research Article Open Access

Accelerating video frames classification with metric based scene segmentation

Abstract

This paper addresses the problem of the efficient classification of images in a video stream in cases, where all of the video has to be labeled. Realizing the similarity of consecutive frames, we introduce a set of simple metrics to measure that similarity. To use these observations for decreasing the number of necessary classifications, we propose a scene segmentation algorithm. Performed experiments have evaluated the acquired scene sizes and classification accuracy resulting from the usage of different similarity metrics with our algorithm. As a result, we have identified those metrics from the considered set, which show the best characteristics for usage in scene segmentation.

Adam Blokus, Jan Cychnerski, Adam Brzeski

To read the full article Download Full Article | Visit Full Article