Learning-Based Local Visual Representation and Indexing

✍ Scribed by Rongrong Ji, Yue Gao, Ling-Yu Duan, Hongxun Yao, Qionghai Dai

• Publisher: Morgan Kaufmann
• Year: 2015
• Tongue: English
• Leaves: 117
• Series: Computer Science Reviews and Trends
• Edition: 1
• Category: Library

✦ Synopsis

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.

• Discusses state-of-the-art procedures in learning-based local visual representation.
• Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system
• Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

✦ Table of Contents

Content:
Front Matter, Pages i-ii
Copyright, Page iv
Preface, Pages vii-ix
List of Figures, Pages xi-xiii
List of Tables, Page xv
List of Algorithms, Page xvii
Chapter 1 - Introduction, Pages 1-15
Chapter 2 - Interest-Point Detection: Beyond Local Scale, Pages 17-40
Chapter 3 - Unsupervised Dictionary Optimization, Pages 41-65
Chapter 4 - Supervised Dictionary Learning via Semantic Embedding, Pages 67-80
Chapter 5 - Visual Pattern Mining, Pages 81-99
Conclusions, Pages 101-102
References, Pages 103-109