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Spatial Verification via Compact Words for Mobile Instance Search

机译：通过紧凑字进行空间验证以进行移动实例搜索

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摘要

Instance search is a retrieval task that searches video segments or images relevant to a certain specific instance (object, person, or location). Selecting more representative visual words is a significant challenge for the problem of instance search, since spatial relations between features are leveraged in many state-of-the-art methods. However, with the popularity of mobile devices it is now feasible to adopt multiple similar photos from mobile devices as a query to extract representative visual words. This paper proposes a novel approach for mobile instance search, by spatial analysis with a few representative visual words extracted from multi-photos. We develop a scheme that applies three criteria, including BM25 with exponential IDF (EBM25), significance in multi-photos and separability to rank visual words. Then, a spatial verification method about position relations is applied to a few visual words to obtain the weight of each photo selected. In consideration of the limited bandwidth and instability of wireless channel, our approach only transmits a few visual words from mobile client to server and the number of visual words varies with bandwidth. We evaluate our approach on Oxford building dataset, and the experimental results demonstrate a notable improvement on average precision over several state-of-the-art methods including spatial coding, query expansion and multiple photos.
机译：实例搜索是一种检索任务，用于搜索与某个特定实例（对象，人或位置）相关的视频片段或图像。由于实例间的空间关系在许多最新方法中得到了利用，因此选择更多具有代表性的视觉单词对于实例搜索是一个重大挑战。但是，随着移动设备的普及，现在可以采用来自移动设备的多张相似照片作为查询以提取代表性的视觉单词。本文提出了一种新的移动实例搜索方法，该方法是通过对从多张照片中提取的一些代表性视觉单词进行空间分析来进行的。我们开发了一种方案，该方案应用了三个标准，包括带指数IDF的BM25（EBM25），在多张照片中的重要性以及对视觉单词进行排名的可分离性。然后，将关于位置关系的空间验证方法应用于几个视觉单词，以获得所选择的每张照片的权重。考虑到有限的带宽和无线通道的不稳定性，我们的方法仅从移动客户端向服务器传输一些视觉单词，视觉单词的数量随带宽而变化。我们在牛津建筑数据集上评估了我们的方法，实验结果表明，与几种最先进的方法（包括空间编码，查询扩展和多张照片）相比，平均精度有了显着提高。

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