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英国诺丁汉大学Guoping Qiu教授报告通知

2013-11-07来源: 智能所浏览次数:

2013研究生学术年会(电院分会场)
Fast Semantic Nearest Neighbour Search- Random Forest for Image Annotation

承办单位:国际智能感知与计算研究中心
              “智能感知与图像理解”教育部重点实验室
               国家111计划创新引智基地
               IEEE西安分会
               IET西安分会
报告地点:主楼Ⅱ-227报告厅
报告时间:2013年11月11日 14:00
报告题目:Fast Semantic Nearest Neighbour Search- Random Forest for Image Annotation
报告人:   Guoping Qiu

报告摘要

  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.

专家简介

  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.

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