Mobile Visual Search Compression With Grassmann Manifold Embedding
Mobile Visual Search Compression With Grassmann Manifold Embedding
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DOI：
10.1109/tcsvt.2018.2881177
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发表时间：
2019-11
影响因子：
8.4
通讯作者：
Zhaobin Zhang;Li Li-Li;Zhu Li;Houqiang Li
中科院分区：
文献类型：
--
作者：
Zhaobin Zhang;Li Li-Li;Zhu Li;Houqiang Li
随着移动电话和平板电脑的日益普及，按捕获查询应用的爆炸性增长要求紧凑地表示查询图像特征。视觉搜索紧凑描述符(CDVS)是ISO/IEC运动图像专家组最近发布的一项标准，它在图像检索应用程序中实现了最先进的性能。然而，它们没有考虑大规模数据库在局部空间中的匹配特性，这可能会降低性能。本文针对基于Grassmann流形的视觉查询，提出了一种更紧凑的尺度不变特征变换(SIFT)描述子表示方法。由于图像内容的巨大变化，使用单个变换来捕获所有信息是不够的。为了获得更有效的表示，首先构建了SIFT流形划分树(SMPT)，将大数据集在多个尺度上划分为小组，目的是捕获更具区分性的信息。然后应用Grassmann流形对SMPT进行修剪并搜索最有特色的变换。实验结果表明，该框架在标准基准CDVS数据集上达到了最好的性能。
With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset.