Learning-Based Local Visual Representation and Indexing [Paperback]

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.

Chapter 1 Introduction
Chapter 2 Context-aware semi-local feature detector
Chapter 3 Unsupervised vocabulary optimization
Chapter 4 Supervised vocabulary learning
Chapter 5 3D Visual Descriptor LearningLearning-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.Professor Rongrong Ji , is the Director of the Intelligent Multimedia Lab at Xiamen University. He is an active researcher pursuing innovations in multimedia, computer vision and pattern recognition. His scholarly work mainly focuses on building cutting-edge computer systems to understand visual scenes, to retrieve visual instances, and to digest human behaviors, with emerging applications to mobile visual search and social media analytics. His recent interests include compact visual descriptor, social media sentiment analysis, and holistic lc5