Dense sampling low-level statistics of local features
Dense sampling low-level statistics of local features
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DOI：
10.1145/1646396.1646419
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
Hideki Nakayama;T. Harada;Y. Kuniyoshi
中科院分区：
文献类型：
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作者：
Hideki Nakayama;T. Harada;Y. Kuniyoshi
近年来，通用图像识别技术在自动图像索引方面得到了广泛的研究。然而，对于实际的大型设置来说，这些工作的计算量太大。因此，为了实现可伸缩性，在性能和计算成本之间取得适当的平衡是非常重要的。近年来，基于关键点袋技术的方法取得了很大的成功，并得到了广泛的应用。然而，在大规模的数据集中，构建视觉词的预处理成本是巨大的。另一方面，基于全局图像特征的方法已经使用了很长时间。由于可以快速提取全局图像特征，因此在非常大的数据集上使用它们相对容易。然而，全局特征方法的性能通常比关键点袋方法差。在本文中，我们提出了一种非常简单但功能强大的方案，通过密集采样局部特征的低水平统计（均值和相关性）来提高全局图像特征的性能。此外，我们使用了一种高度可扩展的学习和分类方法，它比SVM轻得多。尽管我们的方法非常简单，但它取得了与最先进的方法相当的性能。
Recently, generic image recognition techniques are widely studied for automatic image indexing. However, much of these works are computationally too heavy for practical large setup. Thus, it is very important to properly balance the trade-off between performance and computational cost for realizing scalability. In recent years, methods based on the bag-of-keypoints technique have been quite successful and are widely used. However, preprocessing cost for building visual words becomes immense in large scale datasets. On the other hand, methods based on global image features have been used for a long time. Because global image features can be extracted rapidly, it is relatively easy to use them with very large datasets. However, the performance of global feature methods is usually poor compared to bag-of-keypoints. In this paper, we propose a very simple but powerful scheme of boosting the performance of global image features, by densely sampling low-level statistics (mean and correlation) of local features. Also, we use a highly scalable learning and classification method which is substantially lighter than SVM. Our method achieved the performance comparable to state-of-the-art methods in spite of its remarkable simplicity.