Multi-agents Architecture for Semantic Retrieving Video in Distributed Environment
This work addresses video retrieval challenges for users in distributed data settings, but it appears incremental as it builds on existing collaborative and multimodal approaches without claiming major breakthroughs.
The paper tackles the problem of indexing and retrieving video information in distributed environments by proposing a multi-agents architecture that integrates multimodal aspects, semantic annotation, and personalized requests, aiming to improve system performance in a smart way.
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