AI

CH-6 – Abstract Video Stream Processing Theory

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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.

Ali Imran
Over the past 20+ years, I have been working as a software engineer, architect, and programmer, creating, designing, and programming various applications. My main focus has always been to achieve business goals and transform business ideas into digital reality. I have successfully solved numerous business problems and increased productivity for small businesses as well as enterprise corporations through the solutions that I created. My strong technical background and ability to work effectively in team environments make me a valuable asset to any organization.
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