Feature extraction and fusion techniques for multimodal data

Title

Feature extraction and fusion techniques for multimodal data

Creator

Nanjundan, Preethi; Deokar, Ruchira; George, Jossy; Eslamian, Saeid

Description

Integrating multimodal information has become crucial in the big data era for developing a thorough knowledge of complex systems and enhancing decision-making in a variety of fields. The importance of feature extraction and fusion strategies in multimodal learning is examined in this chapter, with particular attention paid to the difficulties and approaches involved in merging several data modalities, including text, pictures, audio, and sensor data. It talks about how conventional feature extraction approaches have evolved into more sophisticated ones like deep learning models for picture and audio data and neural embeddings for text. The chapter also explores several fusion tactics, such as early, late, and intermediate fusion, and focuses on how they are used in domains including sentiment analysis, autonomous cars, healthcare, and multimodal search engines. The chapter highlights future directions, such as lightweight architectures and privacy-preserving techniques, while also addressing contemporary issues, such as managing missing data, scalability, and privacy concerns. The chapter provides a thorough grasp of how feature extraction and fusion aid in the creation of multimodal systems that are more precise, effective, and interpretable by looking at these factors. 2026 Elsevier Inc. All rights reserved.

Source

Multimodal Learning Using Heterogeneous Data;pp.43-56

Date

01-01-2025

Publisher

Elsevier

Subject

autonomous vehicles; deep learning; feature extraction; feature fusion; healthcare applications; multimodal data; Multimodal learning; neural embeddings; privacy-preserving methods; sensor data

Coverage

Nanjundan P., Department of Data Science, CHRIST University, Lavasa Campus, Maharashtra, Pune, India; Deokar R., Department of Data Science, CHRIST University, Karnataka, Bengaluru, India; George J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Eslamian S., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-044327528-9; 978-044327529-6;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-27528-9.00013-9

https://www.scopus.com/pages/publications/105032963774?origin=resultslist