PCA-SIFT

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.


References in zbMATH (referenced in 63 articles )

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  1. Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram: FRIST-flipping and rotation invariant sparsifying transform learning and applications (2017)
  2. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  3. Lindeberg, Tony: Image matching using generalized scale-space interest points (2015)
  4. Kim, Hye Won; Yoo, Suk I.: Defect detection using feature point matching for non-repetitive patterned images (2014)
  5. Lei, Hao; Mei, Kuizhi; Zheng, Nanning; Dong, Peixiang; Zhou, Ning; Fan, Jianping: Learning group-based dictionaries for discriminative image representation (2014)
  6. Liu, Lingqiao; Wang, Lei: HEp-2 cell image classification with multiple linear descriptors (2014) ioport
  7. Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie: A novel biologically inspired local feature descriptor (2014) ioport
  8. Fan, Wentao; Bouguila, Nizar: Infinite Dirichlet mixture models learning via expectation propagation (2013)
  9. Fan, Wentao; Bouguila, Nizar: Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection (2013)
  10. Guo, Yulan; Sohel, Ferdous; Bennamoun, Mohammed; Lu, Min; Wan, Jianwei: Rotational projection statistics for 3D local surface description and object recognition (2013)
  11. Kim, Bongjoe; Yoo, Hunjae; Sohn, Kwanghoon: Exact order based feature descriptor for illumination robust image matching (2013) ioport
  12. Kimura, T.; Tokuda, T.; Nakada, Y.; Nokajima, T.; Matsumoto, T.; Doucet, A.: Expectation-maximization algorithms for inference in Dirichlet processes mixture (2013)
  13. Li, Peihua: Tensor-SIFT based Earth mover’s distance for contour tracking (2013)
  14. Qian, Jianjun; Yang, Jian; Gao, Guangwei: Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction (2013) ioport
  15. Sedai, S.; Bennamoun, M.; Huynh, D.Q.: Discriminative fusion of shape and appearance features for human pose estimation (2013) ioport
  16. Song, Xiaohu; Muselet, Damien; Trémeau, Alain: Affine transforms between image space and color space for invariant local descriptors (2013) ioport
  17. Yin, Dongsheng; Liu, Debo: Content-based image retrial based on Hadoop (2013) ioport
  18. Zhang, Xiangrong; Yang, Yang; Jiao, L.C.; Dong, Feng: Manifold-constrained coding and sparse representation for human action recognition (2013) ioport
  19. Zhu, Chao; Bichot, Charles-Edmond; Chen, Liming: Image region description using orthogonal combination of local binary patterns enhanced with color information (2013) ioport
  20. Albarelli, Andrea; Rodolà, Emanuele; Torsello, Andrea: Imposing semi-local geometric constraints for accurate correspondences selection in structure from motion: a game-theoretic perspective (2012) ioport

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