Video Classification Using Normalized Information Distance

Abstract

There has been a vast collection of multimedia resources on the net. This has opened an opening for researchers to explore and advance the science in the field of research in storing, handling, and retrieving digital videos. Video classification and segmentation are fundamental steps for efficient accessing; retrieving, browsing and compressing large amount of video data. The basic operation video analysis is to design a system that can accurately and automatically segments video material into shots and scenes. This paper presents a detailed video segmentation technique based on pervious researches which lacks performance and since some of the videos is stored in a compressed form using the normalized information distance (NID) which approximates the value of a theoretical distance between objects using the Kolmogrov complexity theory. This technique produced a better result in reference to performance, high recall of 95.5% and a precision of 89.7%