Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark   Search

CORC > 北京大学 > 信息科学技术学院

	Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search
	Duan, Ling-Yu ; Chen, Jie ; Ji, Rongrong ; Huang, Tiejun ; Gao, Wen
刊名	ai magazine
	2013
关键词	LOCAL DESCRIPTORS RECOGNITION FEATURES
英文摘要	Along with the ever-growing computational power of mobile devices, mobile visual search has undergone an evolution in techniques and applications. A significant trend is low bit rate visual search, where compact visual descriptors are extracted directly over a mobile and delivered as queries rather than raw images to reduce the query transmission latency. In this article, we introduce our work on low bit rate mobile landmark search, in which a compact yet discriminative landmark image descriptor is extracted by using a location context such as GPS, crowd-sourced hotspot WLAN, and cell tower locations. The compactness originates from the bag-of-words image representation, with off line learning from geotagged photos from online photo-sharing websites including Flickr and Panoramio. The learning process involves segmenting the landmark photo collection by discrete geographical regions using a Gaussian mixture model and then boosting a ranking-sensitive vocabulary within each region, with "entropy"-based feedback on the compactness of the descriptor to refine both phases iteratively. In online search, when entering a geographical region, the code book in a mobile device is downstream adapted to generate extremely compact descriptors with promising discriminative ability. We have deployed landmark search apps to both HTC and iPhone mobile phones, accessing a database of a million scale images in typical areas like Beijing, New York, and Barcelona, and others. Our descriptor outperforms alternative compact descriptors (Chen et al. 2009; Chen et al., 2010; Chandrasekhar et al. 2009a; Chandrasekhar et al. 2009b) by significant margins. Beyond landmark search, this article will summarize the MPEG standarization progress of compact descriptor for visual search (CDVS) (Yuri et al. 2010; Yuri et al. 2011) toward application interoperability.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000336891800006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; SCI(E); EI; 0; ARTICLE; 2; 67-85; 34
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/391534]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Duan, Ling-Yu,Chen, Jie,Ji, Rongrong,et al. Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search[J]. ai magazine,2013.
APA	Duan, Ling-Yu,Chen, Jie,Ji, Rongrong,Huang, Tiejun,&Gao, Wen.(2013).Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search.ai magazine.
MLA	Duan, Ling-Yu,et al."Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search".ai magazine (2013).