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首页> 外文期刊>Emerging and Selected Topics in Circuits and Systems, IEEE Journal on >Embedding Multi-Order Spatial Clues for Scalable Visual Matching and Retrieval
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Embedding Multi-Order Spatial Clues for Scalable Visual Matching and Retrieval

机译：嵌入多阶空间线索以进行可扩展的视觉匹配和检索

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摘要

Matching duplicate visual contents among images serves as the basis of many vision tasks. Researchers have proposed different local descriptors for image matching, e.g., floating point descriptors like SIFT, SURF, and binary descriptors like ORB and BRIEF. These descriptors either suffer from relatively expensive computation or limited robustness due to the compact binary representation. This paper studies how to improve the matching efficiency and accuracy of floating points descriptors and the matching accuracy of binary descriptors. To achieve this goal, we embed the spatial clues among local descriptors to a novel local feature, i.e., multi-order visual phrase which contains two complementary clues: 1) the center visual clues extracted at each image keypoint and 2) the neighbor visual and spatial clues of multiple nearby keypoints. Different from existing visual phrase features, two multi-order visual phrases are flexibly matched by first matching their center visual clues, then estimating a match confidence by checking the spatial and visual consistency of their neighbor keypoints. Therefore, multi-order visual phrase does not scarify the repeatability of classic visual word and is more robust to the quantization error than existing visual phrase features. We extract multi-order visual phrases from both SIFT and ORB and test them in image matching and retrieval tasks on UKbench, Oxford5K, and 1 million distractor images collected from Flickr. Comparisons with recent retrieval approaches clearly demonstrate the competitive accuracy and significantly better efficiency of our approaches.
机译：在图像之间匹配重复的视觉内容是许多视觉任务的基础。研究人员提出了用于图像匹配的不同局部描述符，例如SIFT，SURF等浮点描述符以及ORB和Brief的二进制描述符。这些描述符由于紧凑的二进制表示而遭受了相对昂贵的计算或有限的鲁棒性。本文研究如何提高浮点描述符的匹配效率和精度以及二进制描述符的匹配精度。为了实现这一目标，我们将局部描述符之间的空间线索嵌入到一个新颖的局部特征中，即包含两个互补线索的多级视觉短语：1）在每个图像关键点提取的中心视觉线索； 2）邻居视觉线索和附近多个关键点的空间线索。与现有的视觉短语功能不同，两个多阶视觉短语通过首先匹配其中心视觉线索，然后通过检查其相邻关键点的空间和视觉一致性来估计匹配置信度，从而灵活地进行匹配。因此，多阶视觉短语不会破坏经典视觉单词的可重复性，并且比现有视觉短语功能更能抵抗量化误差。我们从SIFT和ORB中提取多阶视觉短语，并在UKbench，Oxford5K和从Flickr收集的100万个干扰物图像的图像匹配和检索任务中对其进行测试。与最新检索方法的比较清楚地表明了我们方法的竞争准确性和明显更高的效率。

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