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引用次数: 22

Abstract

The study of efficient image representations has attracted significant interest due to the computational needs of large-scale applications. In this paper we study the performance of the recently proposed VLAD method for aggregating local image descriptors when combined with SURF features, in the domain of image search. The experiments show that when SURF features are used as local image descriptors, VLAD attains better performance compared to using SIFT features. We also study how the average number of local image descriptors extracted per image affects the performance and show that by controlling this number we are able to adjust the trade off between feature extraction time and search accuracy. Finally, we examine the retrieval performance of the proposed scheme with varying levels of distractor images.
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结合冲浪特征与VLAD矢量进行图像搜索的实证研究
由于大规模应用的计算需求,高效图像表示的研究引起了人们的极大兴趣。在本文中,我们研究了最近提出的VLAD方法结合SURF特征聚合局部图像描述符在图像搜索领域的性能。实验表明,当使用SURF特征作为局部图像描述符时,VLAD比使用SIFT特征获得了更好的性能。我们还研究了每张图像提取的局部图像描述符的平均数量如何影响性能,并表明通过控制这个数量,我们能够调整特征提取时间和搜索精度之间的权衡。最后,我们在不同级别的干扰图像中检验了所提出方案的检索性能。
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