Video Data Visualization System: Semantic Classification And Personalization

TL;DR

This paper introduces an intelligent video visualization system that uses semantic classification and user profiling to enhance retrieval and exploration of large video datasets through graph-based visualization.

Contribution

It presents a novel semantic classification approach integrated with personalized visualization for large-scale video data exploration.

Findings

Effective semantic classification of videos achieved

Personalized visualization improves user interaction

Graph-based visualization enhances video data exploration

Abstract

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.