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20150506-13 王井东:Composite Quantization for Approximate Nearest Neighbor Searc ...

2015-5-2 19:25| 发布者: 贾伟中科院合肥| 查看: 7111| 评论: 0

摘要: 报告嘉宾1:王井东(MSRA) 主持人:张兆翔(北航) 报告题目:Composite Quantization for Approximate Nearest Neighbor Search 文章信息:Ting Zhang, Chao Du, Jingdong Wang, Composite Quantization for Appro ...
【15-13期VALSE Webinar活动】
报告嘉宾1:王井东微软亚洲研究院
主持人:张兆翔(北京航天航空大学)
报告题目:Composite Quantization for Approximate Nearest Neighbor Search http://valser.org/webinar/slide/slides/20150506/Jindong%20Wang_ValseWebinar.pdf
文章信息:Ting Zhang, Chao Du, Jingdong Wang, Composite Quantization for Approximate Nearest Neighbor Search, ICML2014.
报告时间:2015年5月6日晚20:00(北京时间)
报告摘要: In this talk, I will present our recent work on approximate nearest neighbor search: a compact coding approach, composite quantization . The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements. To efficiently compute the approximate distance of a query to a database vector using the short code, our approach introduces an extra constraint, constant inter-dictionary-element-product, resulting in that approximating the distance only using the distance of the query to each selected element is enough for nearest neighbor search.
报告人简介: Jingdong Wang is a Lead Researcher at the Visual Computing Group, Microsoft Research Asia. He received the M.Eng. and B.Eng. degrees in Automation from the Department of Automation, Tsinghua University, Beijing, China, in 2001 and 2004, respectively, and the PhD degree in Computer Science from the Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, in 2007. His areas of interest include computer vision, machine learning, pattern recognition, and multimedia computing. In particular, he has worked on kernel methods, semi-supervised learning, data clustering, image segmentation, and image and video presentation, management and search. At present, he is mainly working on the Big Media project, including large-scale indexing and clustering, Web image search and mining, and visual understanding such as salient object detection, image recognition, face alignment and recognition.

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