Fast Semantic Nearest Neighbour Search- Random Forest for Image Annotation
报告题目：Fast Semantic Nearest Neighbour Search- Random Forest for Image Annotation
报告人： Guoping Qiu
This talk presents a novel method for automatic image annotation (which can also be understood as a generic fast semantic nearest neighbour search method). We use the tags contained in the training images as the supervising information to guide the generation of random trees, thus making the retrieved nearest neighbor images not only visually alike but also semantically related. Different from conventional decision tree methods, which fuse the information contained at each leaf node individually, our method treats the random forest as a whole, and introduces the new concepts of semantic nearest neighbors (SNN) and semantic similarity measure (SSM). We introduce a method to annotate an image from the tags of its SNN based on SSM and have developed a novel learning to rank algorithm to systematically assign the optimal tags to the image. The new technique is intrinsically scalable and we will present experimental results to demonstrate that it is competitive to state of the art image annotation methods.
Guoping Qiu is a Chair of Digital Technology, Head of the Division of Computer Science and Acting Dean of the Faculty of Science and Engineering at the University of Nottingham China Campus. He is also with the School of Computer Science at the University of Nottingham, Nottingham, UK. He has taught in universities in the UK and Hong Kong and also consulted for multinational companies in Europe, Hong Kong and China. His research interests include image processing, pattern recognition, multimedia signal analysis and digital economy. He has published widely and also holds European and US patents.