Location Discriminative Vocabulary Coding for Mobile Landmark Search
Location Discriminative Vocabulary Coding for Mobile Landmark Search
复制标题
用于移动地标搜索的位置辨别词汇编码
DOI：
10.1007/s11263-011-0472-9
复制
发表时间：
2012-02-01
影响因子：
19.5
通讯作者：
Gao, Wen
中科院分区：
文献类型：
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作者：
Ji, Rongrong;Duan, Ling-Yu;Gao, Wen
随着移动的设备的普及，近年来已经见证了移动的地标搜索的新兴潜力。在这种情况下，用户体验很大程度上取决于无线链路上查询传输的效率。由于发送查询照片是耗时的，最近的工作已经提出了直接在移动的端上提取紧凑的视觉描述符，以实现低比特率传输。通常，这些描述符仅基于查询的视觉内容来提取，并且很少利用来自移动的端的位置线索。在本文中，我们提出了一个位置判别词汇编码（LDVC）计划，它实现了极低的比特率查询传输，判别地标描述，以及可扩展的描述符交付在一个统一的框架。我们的第一个贡献是一个紧凑的和位置判别的视觉地标描述符，这是离线学习两步：首先，我们采用谱聚类分割城市地图到不同的地理区域，视觉和地理相似性融合，以优化城市规模的地理标记照片的分区。其次，我们建议使用两种方案来学习每个区域中的LDVC：（1）排名敏感PCA和（2）排名敏感词汇提升。这两种方案都嵌入了位置线索来学习一个紧凑的描述符，通过替换原始的高维签名来最小化检索排名损失。我们的第二个贡献是一个位置感知的在线词汇适应：我们存储在移动的端，这是有效地适应区域特定的LDVC编码一旦一个移动终端进入一个给定的区域一个单一的词汇。学习的LDVC界标描述符是非常紧凑的（通常为10-50位，具有算术编码），并且比现有技术的描述符执行上级。我们在一个真实世界的移动的地标搜索原型中实现了该框架，并在一个覆盖北京、纽约市、拉萨、新加坡和佛罗伦萨等典型地区的百万级地标数据库中进行了验证。
With the popularization of mobile devices, recent years have witnessed an emerging potential for mobile landmark search. In this scenario, the user experience heavily depends on the efficiency of query transmission over a wireless link. As sending a query photo is time consuming, recent works have proposed to extract compact visual descriptors directly on the mobile end towards low bit rate transmission. Typically, these descriptors are extracted based solely on the visual content of a query, and the location cues from the mobile end are rarely exploited. In this paper, we present a Location Discriminative Vocabulary Coding (LDVC) scheme, which achieves extremely low bit rate query transmission, discriminative landmark description, as well as scalable descriptor delivery in a unified framework. Our first contribution is a compact and location discriminative visual landmark descriptor, which is offline learnt in two-step: First, we adopt spectral clustering to segment a city map into distinct geographical regions, where both visual and geographical similarities are fused to optimize the partition of city-scale geo-tagged photos. Second, we propose to learn LDVC in each region with two schemes: (1) a Ranking Sensitive PCA and (2) a Ranking Sensitive Vocabulary Boosting. Both schemes embed location cues to learn a compact descriptor, which minimizes the retrieval ranking loss by replacing the original high-dimensional signatures. Our second contribution is a location aware online vocabulary adaption: We store a single vocabulary in the mobile end, which is efficiently adapted for a region specific LDVC coding once a mobile device enters a given region. The learnt LDVC landmark descriptor is extremely compact (typically 10–50 bits with arithmetical coding) and performs superior over state-of-the-art descriptors. We implemented the framework in a real-world mobile landmark search prototype, which is validated in a million-scale landmark database covering typical areas e.g. Beijing, New York City, Lhasa, Singapore, and Florence.