Content based video retrieval systems

TL;DR

This survey reviews features and similarity measurement methods used in content-based video retrieval systems, emphasizing feature selection's importance in improving retrieval efficiency and identifying current research challenges.

Contribution

It provides a comprehensive overview of video features, similarity measures, and highlights research issues in content-based video retrieval systems.

Findings

Various features can be extracted for indexing and retrieval

Feature selection impacts retrieval efficiency and accuracy

Current research issues include feature robustness and scalability

Abstract

With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems, as users shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval regardless of video attributes being under consideration. These features are intended for selecting, indexing and ranking according to their potential interest to the user. Good features selection also allows the time and space costs of the retrieval process to be reduced. This survey reviews the interesting features that can be extracted from video data for indexing and retrieval along with similarity measurement methods. We also identify present research issues in area of content based video retrieval systems.