Multi-agents Architecture for Semantic Retrieving Video in Distributed Environment

TL;DR

This paper introduces a multi-agents system designed for efficient, semantic-based video retrieval in distributed environments, addressing challenges like data distribution, content adaptation, and user feedback to enhance retrieval performance.

Contribution

It proposes an extensible multi-agents architecture integrating essential tools for video classification, indexing, and retrieval, tailored for distributed, multimodal, and semantic video data.

Findings

Improved video retrieval accuracy through semantic annotation.

Enhanced system adaptability with active user feedback.

Effective handling of distributed video data sources.

Abstract

This paper presents an integrated multi-agents architecture for indexing and retrieving video information.The focus of our work is to elaborate an extensible approach that gathers a priori almost of the mandatory tools which palliate to the major intertwining problems raised in the whole process of the video lifecycle (classification, indexing and retrieval). In fact, effective and optimal retrieval video information needs a collaborative approach based on multimodal aspects. Clearly, it must to take into account the distributed aspect of the data sources, the adaptation of the contents, semantic annotation, personalized request and active feedback which constitute the backbone of a vigorous system which improve its performances in a smart way