: looking for a needle in a haystack: a content-based video big data retrieval system in the cloud

Abstract

The rapid proliferation of video data from various sources underscore the pressing need for effective Content-based Video Retrieval (CBVR) systems. Traditional retrieval methodologies are increasingly inadequate for managing the complexities and scale of video big data, which necessitates the development of advanced distributed computing frameworks. This study identifies and addresses critical challenges in CBVR , specifically the implementation of lambda architecture for the retrieval of both streaming and batch video data, the enhancement of in-memory analytics for video data structures, and the efficient indexing of heterogeneous video features. We propose , a novel scale-out system which integrates state-of-the-art big data technologies with deep learning algorithms. The system architecture is inspired by lambda principles and is designed to facilitate both near real-time and offline video indexing and retrieval. Key contributions of this research include: (1) the formulation of a lambda-style architecture tailored for video big data, (2) the development of an in-memory processing framework that provides a high-level abstraction for video analytics, (3) the introduction of a unified distributed indexer, termed Distributed Encoded Deep Feature Indexer (DEFI), capable of indexing multi-type features from both streaming and batch video datasets, and (4) a comprehensive bottleneck analysis of the proposed system. Performance evaluations utilizing three benchmark datasets demonstrate the system’s effectiveness, revealing insights into performance bottlenecks related to storage, video stream acquisition, processing, and indexing. This research provides a foundational framework for scalable and efficient video analytics, significantly advancing the state-of-the-art in cloud-based CBVR systems.