School of Computer Science and Technology, Huazhong University of Science & Technology, Wuhan, People's Republic of China.
PLoS One. 2014 Jun 3;9(6):e98806. doi: 10.1371/journal.pone.0098806. eCollection 2014.
Mobile Visual Location Recognition (MVLR) has attracted a lot of researchers' attention in the past few years. Existing MVLR applications commonly use Query-by-Example (QBE) based image retrieval principle to fulfill the location recognition task. However, the QBE framework is not reliable enough due to the variations in the capture conditions and viewpoint changes between the query image and the database images. To solve the above problem, we make following contributions to the design of a panorama based on-device MVLR system. Firstly, we design a heading (from digital compass) aware BOF (Bag-of-features) model to generate the descriptors of panoramic images. Our approach fully considers the characteristics of the panoramic images and can facilitate the panorama based on-device MVLR to a large degree. Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method. While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly. Thirdly, we also release a panoramas database as well as a set of test panoramic quires which can be used as a new benchmark to facilitate further research in the area. Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.
移动视觉定位识别 (MVLR) 在过去几年中引起了许多研究人员的关注。现有的 MVLR 应用程序通常使用基于示例查询 (QBE) 的图像检索原理来完成定位识别任务。然而，由于查询图像和数据库图像之间的捕获条件和视角变化，QBE 框架不够可靠。为了解决上述问题，我们为基于设备的全景 MVLR 系统的设计做出了以下贡献。首先，我们设计了一个基于方向（来自数字指南针）感知的 BOF（特征袋）模型来生成全景图像的描述符。我们的方法充分考虑了全景图像的特点，可以在很大程度上促进基于设备的全景 MVLR。其次，为了在移动设备上直接搜索高维视觉描述符，我们提出了一种有效的基于双线性压缩感知的编码方法。我们的算法既快速又准确，足以在设备上实现，同时还可以显著减少投影矩阵的内存使用。第三，我们还发布了一个全景数据库以及一组测试全景查询，可以作为一个新的基准来促进该领域的进一步研究。实验结果证明了所提出的方法在基于设备的 MVLR 应用中的有效性。
