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A Low Transmission Overhead Framework of Mobile Visual Search Based on Vocabulary Decomposition

机译：基于词汇分解的移动视觉搜索低传输开销框架

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摘要

Due to the bandwidth limitation in wireless networks, transmission overhead is a big problem in Mobile Visual Search (MVS). Existing work proposes transmitting the compressed local feature descriptors instead of the query image to reduce the transmission overhead. Although many kinds of compressed descriptors are proposed, designing a suitable lossless compressed descriptor has proven elusive. In this paper, we propose a novel framework for MVS with low transmission overhead rather than focusing on compressed descriptors. The key point of the proposed framework is to migrate the vector quantization in the bag of visual words model from the server to the client. In this framework, no matter what descriptors are used, the client only transmits the ID numbers of the visual words to the server, thereby reaching the minimal possible transmission overhead. To achieve this goal, we present vocabulary decomposition by which we can decompose the large vocabulary into several small ones satisfying storage constraints on mobile devices. In this paper, we first formulate vocabulary decomposition as an optimization problem. We then present Joint Product Quantization (JPQ) and Joint Optimized Product Quantization (JOPQ) to address the proposed optimization problem. Finally , we conduct a large number of simulation experiments and real experiments. The experimental results show that the proposed framework outperforms the existing framework by reducing more than 95% of the transmission overhead.
机译：由于无线网络中的带宽限制，传输开销是移动视觉搜索（MVS）中的一个大问题。现有工作提出传输压缩的局部特征描述符而不是查询图像，以减少传输开销。尽管提出了多种压缩描述符，但事实证明，设计合适的无损压缩描述符是难以实现的。在本文中，我们提出了一种用于MVS的新颖框架，该框架具有较低的传输开销，而不是关注压缩描述符。所提出框架的重点是将视觉单词模型包中的矢量量化从服务器迁移到客户端。在此框架中，无论使用什么描述符，客户端都仅将视觉单词的ID号发送到服务器，从而达到最小的传输开销。为了实现此目标，我们提出了词汇分解功能，通过该功能我们可以将大词汇量分解为几个小词，以满足移动设备上的存储限制。在本文中，我们首先将词汇分解公式化为一个优化问题。然后，我们提出联合产品量化（JPQ）和联合优化产品量化（JOPQ），以解决所提出的优化问题。最后，我们进行了大量的模拟实验和真实实验。实验结果表明，通过减少95％以上的传输开销，该框架优于现有框架。

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