Important Note: This article is part of a series in which TechReport.us discusses the theory of Video Stream Matching.
Abstract
The comparison of video sequences holds significance in numerous multimedia information systems. The typical basis for similarity measurement involves correlation with perceptual similarities or differences within video sequences, or with similarities or differences in semantic measures associated with the sequences.
In content-based similarity analysis, video data are expressed through various features, and similarity matching is executed by quantifying the relationships between features in the target and query video shots, using either individual features or feature combinations.
This study focuses on similarity analysis by assessing similarities among images. In this approach, key frames are extracted for each video shot, and the similarity among video shots is determined by comparing these key frames.
The extracted features encompass image histograms, slopes, edges, and wavelets. Both individual features and feature combinations are employed in similarity matching through Statistical Models. These models include NORM, MEAN, VARIANCE, and KS-TEST.
