移动视觉搜索关键技术研究

项目名称： 移动视觉搜索关键技术研究

项目编号： No.61271311

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 段凌宇

作者单位： 北京大学

项目金额： 80万元

中文摘要： 近年来随着移动互联网、智能终端以及云计算的迅速崛起，越来越多的人希望在移动中高速接入互联网，获取实时信息。移动视觉搜索毫无疑问成为未来移动世界中有影响的基础技术之一。移动视觉搜索是指利用移动终端获取的图像或视频作为查询，通过移动互联网检索出感兴趣信息的检索方式。人们通过移动视觉搜索技术，可以快速便捷地采集现实世界的视觉对象信息，从移动互联网获取感兴趣的关联信息。尽管移动视觉搜索技术已经面世，仍未得到大范围地应用。该项目将定位移动视觉搜索面临的技术瓶颈与挑战，包括无线带宽压力、移动端电池消耗、查询迟滞、大规模索引文件、移动端有限计算资源、拍照姿态光线多样性等。从移动端、服务端、用户三方面，对紧凑视觉描述子、多尺度量化、视觉主题模型、视觉搜索性能估计、分布式索引等关键环节开展研究。该项目总体目标是提升移动视觉搜索各项关键技术性能，萃取科学问题，从解决方案、理论方法两方面开展研究。

中文关键词： 移动视觉检索；局部视觉特征聚合；局部视觉特征压缩；海量图像索引；视觉检索国际标准

英文摘要： With the significant advance of Mobile Internet, smart mobile devicesas well as cloud computing in recent years, more and more people prefer to surf the Internet via a mobile device and accomplishthe on-the-go search through Mobile Internet to find out useful information anytime and anywhere. Clearly,mobile search is becoming one of the most promising technologies in the daily life of mobile users. In particular, mobile visual search is meant to snap a picture via an embedded camera on a mobile deviceas a visual query to retrieve the information of interest (say, landmarks, documents, commerce products, etc.) via Internet. However，the emerging technologiesin mobile visual search have not yet beenfully developed or applied in a wide range of deserved domains in spite of pervasive mobile computing scenarios. Our proposed project aims to address key challenges in mobile visual search, involvingwireless (3G) bandwidth constraints, prominent battery consumption, the large-scale image indexing, the limited computational power as well as the diversity in image capturing and lighting. From the perspectives of mobile devices, the server end as well as mobile users, we propose to perform comprehensive and cutting-edge research regarding the practical solutions as well as the arising theoretical problems in visual features

英文关键词： Mobile visual retrieval；Local visual feature aggregation；Local visual feature compression；Image Indexing；The MPEG CDVS standard