Fine-Grained Image Analysis: Modern Approaches

2023
ISBN: 978-3-031-31376-9
Verlag: Springer Nature Switzerland

Buch, Englisch, 206 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 378 g

Reihe: Synthesis Lectures on Computer Vision

This book provides a comprehensive overview of the fine-grained image analysis research and modern approaches based on deep learning, spanning the full range of topics needed for designing operational fine-grained image systems. The author begins by providing detailed background information on FGIA, focusing on recognition and retrieval. The author also provides the fundamentals of convolutional neural networks to further make it easier for readers to understand the technical content in the book. The book introduces the main technical paradigms, technological developments, and representative approaches of fine-grained image recognition and fine-grained image retrieval. The author covers multiple popular research topics and includes cross-domain knowledge. The book also highlights advanced applications and topics for future research.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Wei, Xiu-Shen

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Introduction.- Background.- Benchmark Datasets.- Fine-Grained Image Recognition.- Fine-Grained Image Retrieval.- Resources and Future Work.- Index.

Über Autor(innen)

Xiu-Shen Wei, Ph.D., is a Professor at Southeast University’s School of Computer Science and Engineering. He received his Ph.D. degree from Nanjing University. Dr. Wei previously served as the Founding Director at Megvii Research Nanjing, Megvii Technology. He was also a visiting Scholar at The University of Adelaide. Dr. Wei’s research interests include deep convolutional neural networks, fine-grained visual analysis, long-tailed distribution learning, general object detection, and weakly supervised learning.