Residual enhanced visual vector as a compact signature for mobile visual search
Residual enhanced visual vector as a compact signature for mobile visual search
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DOI：
10.1016/j.sigpro.2012.06.005
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发表时间：
2013-08-01
影响因子：
4.4
通讯作者：
Girod, Bernd
中科院分区：
文献类型：
--
作者：
Chen, David;Tsai, Sam;Girod, Bernd
许多移动视觉搜索（MVS）系统将查询数据从移动设备传输到远程服务器，并搜索服务器上托管的数据库。在本文中，我们提出了一种在移动设备上直接搜索大型数据库的新架构，它可为独立于网络、低延迟以及受隐私保护的图像检索带来诸多益处。设备端检索的一个关键挑战是在移动设备有限的随机存取存储器（RAM）中存储大型数据库。为应对这一挑战，我们开发了一种新的紧凑且具有判别力的图像签名，称为残差增强视觉向量（REVV），它针对在移动设备上可快速提取的局部特征集进行了优化。在移动视觉搜索环境中，REVV优于现有的紧凑数据库构建方法，并且在大规模检索中能达到与使用25倍更多内存的词汇树相似的检索精度。我们利用REVV设计并构建了一个用于精确、大规模地标识别的移动增强现实系统。使用REVV进行快速的设备端搜索使我们的系统无论外部网络条件如何，每个查询都能实现约1秒的延迟。REVV的紧凑性使其还能很好地作为一种低比特率签名，在需要时可传输到远程服务器或从远程服务器接收，以便高效扩展本地数据库搜索。（C）2012爱思唯尔B.V.保留所有权利。
Many mobile visual search (MVS) systems transmit query data from a mobile device to a remote server and search a database hosted on the server. In this paper, we present a new architecture for searching a large database directly on a mobile device, which can provide numerous benefits for network-independent, low-latency, and privacy-protected image retrieval. A key challenge for on-device retrieval is storing a large database in the limited RAM of a mobile device. To address this challenge, we develop a new compact, discriminative image signature called the Residual Enhanced Visual Vector (REVV) that is optimized for sets of local features which are fast to extract on mobile devices. REVV outperforms existing compact database constructions in the MVS setting and attains similar retrieval accuracy in large-scale retrieval as a Vocabulary Tree that uses 25x more memory. We have utilized REVV to design and construct a mobile augmented reality system for accurate, large-scale landmark recognition. Fast on-device search with REVV enables our system to achieve latencies around 1 s per query regardless of external network conditions. The compactness of REVV allows it to also function well as a low-bitrate signature that can be transmitted to or from a remote server for an efficient expansion of the local database search when required. (C) 2012 Elsevier B.V. All rights reserved.