PCA-SIFT: a more distinctive representation for local image descriptors
PCA-SIFT: a more distinctive representation for local image descriptors
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DOI：
10.1109/cvpr.2004.183
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发表时间：
2004-06
期刊：
影响因子：
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通讯作者：
Yan Ke;R. Sukthankar
中科院分区：
文献类型：
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作者：
Yan Ke;R. Sukthankar
稳定的局部特征检测与表示是许多图像配准和目标识别算法的基本组成部分。米科拉伊奇克和施密德（2003年6月）近期评估了多种方法，并确定[D. G. 洛威，1999]的尺度不变特征变换（SIFT）算法对常见图像变形的抗性最强。本文研究（并改进了）SIFT所使用的局部图像描述符。与SIFT类似，我们的描述符对特征点邻域内图像梯度的显著方面进行编码；然而，我们不是使用SIFT的平滑加权直方图，而是将主成分分析（PCA）应用于归一化梯度块。我们的实验表明，基于PCA的局部描述符比标准的SIFT表示更具独特性，对图像变形更具鲁棒性，并且更紧凑。我们还给出了结果，表明在图像检索应用中使用这些描述符可提高准确性和加快匹配速度。
Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.