PCA-SIFT: A More Distinctive Representation for Local Image Descriptors Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid recently evaluated a variety of approaches and identified the SIFT 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.

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  1. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  2. Liu, Jinping; He, Jiezhou; Tang, Zhaohui; Xu, Pengfei; Zhang, Wuxia; Gui, Weihua: Characterization of complex image spatial structures based on symmetrical Weibull distribution model for texture pattern classification (2018)
  3. Rodríguez, Mariano; Delon, Julie; Morel, Jean-Michel: Covering the space of tilts. Application to affine invariant image comparison (2018)
  4. Bohi, Amine; Prandi, Dario; Guis, Vincente; Bouchara, Frédéric; Gauthier, Jean-Paul: Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition (2017)
  5. Özyeşil, Onur; Voroninski, Vladislav; Basri, Ronen; Singer, Amit: A survey of structure from motion (2017)
  6. Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram: FRIST-flipping and rotation invariant sparsifying transform learning and applications (2017)
  7. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  8. Lindeberg, Tony: Image matching using generalized scale-space interest points (2015)
  9. Yang, Lian; Lu, Zhangping: A new scheme for keypoint detection and description (2015)
  10. Kim, Hye Won; Yoo, Suk I.: Defect detection using feature point matching for non-repetitive patterned images (2014)
  11. Lei, Hao; Mei, Kuizhi; Zheng, Nanning; Dong, Peixiang; Zhou, Ning; Fan, Jianping: Learning group-based dictionaries for discriminative image representation (2014)
  12. Liu, Lingqiao; Wang, Lei: HEp-2 cell image classification with multiple linear descriptors (2014) ioport
  13. Mou, Wei; Wang, Han; Seet, Gerald: Robust homography estimation based on nonlinear least squares optimization (2014)
  14. Qu, Xiujie; Zhao, Fei; Zhou, Mengzhe; Huo, Haili: A novel fast and robust binary affine invariant descriptor for image matching (2014)
  15. Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie: A novel biologically inspired local feature descriptor (2014) ioport
  16. Fan, Wentao; Bouguila, Nizar: Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection (2013)
  17. Fan, Wentao; Bouguila, Nizar: Infinite Dirichlet mixture models learning via expectation propagation (2013)
  18. Guo, Yulan; Sohel, Ferdous; Bennamoun, Mohammed; Lu, Min; Wan, Jianwei: Rotational projection statistics for 3D local surface description and object recognition (2013)
  19. Kim, Bongjoe; Yoo, Hunjae; Sohn, Kwanghoon: Exact order based feature descriptor for illumination robust image matching (2013) ioport
  20. Kimura, T.; Tokuda, T.; Nakada, Y.; Nokajima, T.; Matsumoto, T.; Doucet, A.: Expectation-maximization algorithms for inference in Dirichlet processes mixture (2013)

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