Learning-Based Local Visual Representation and Indexing

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.

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 scene.
He has served as the guest editor of IEEE MultiMedia Magazine, Neurocomputing, Signal Processing, ACM Multimedia Systems, and the Journal of Multimedia Tools and Applications. In addition he has served as the technical session chair at numerous conferences including, International Conference on Multimedia Retrieval 2014, Visual Communications and Image Processing 2013 and the 2012 Pacific-Rim Conference on Multimedia, etc. He has over 900 Citations and a Google Scholar h-index of 15.