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 77 articles )

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  1. Fan, Wentao; Bouguila, Nizar; Chen, Yewang; Chen, Ziyi: (L_2) normalized data clustering through the Dirichlet process mixture model of von Mises distributions with localized feature selection (2020)
  2. Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo: Scale, translation and rotation invariant wavelet local feature descriptor (2019)
  3. Chi, Jianning; Yu, Xiaosheng; Zhang, Yifei; Wang, Huan: A novel local human visual perceptual texture description with key feature selection for texture classification (2019)
  4. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  5. 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)
  6. Rodríguez, Mariano; Delon, Julie; Morel, Jean-Michel: Covering the space of tilts. Application to affine invariant image comparison (2018)
  7. Xidao, Luan; Yuxiang, Xie; Lili, Zhang; Xin, Zhang; Chen, Li; Jingmeng, He: An image similarity acceleration detection algorithm based on sparse coding (2018)
  8. 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)
  9. Özyeşil, Onur; Voroninski, Vladislav; Basri, Ronen; Singer, Amit: A survey of structure from motion (2017)
  10. Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram: FRIST-flipping and rotation invariant sparsifying transform learning and applications (2017)
  11. Feng, Yiliu; Liu, Yafei; Liu, Hengzhu: B-SIFT: a simple and effective SIFT for real-time application (2016)
  12. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  13. Lindeberg, Tony: Image matching using generalized scale-space interest points (2015)
  14. Yang, Lian; Lu, Zhangping: A new scheme for keypoint detection and description (2015)
  15. Kim, Hye Won; Yoo, Suk I.: Defect detection using feature point matching for non-repetitive patterned images (2014)
  16. Lei, Hao; Mei, Kuizhi; Zheng, Nanning; Dong, Peixiang; Zhou, Ning; Fan, Jianping: Learning group-based dictionaries for discriminative image representation (2014)
  17. Liu, Lingqiao; Wang, Lei: HEp-2 cell image classification with multiple linear descriptors (2014) ioport
  18. Mou, Wei; Wang, Han; Seet, Gerald: Robust homography estimation based on nonlinear least squares optimization (2014)
  19. Qu, Xiujie; Zhao, Fei; Zhou, Mengzhe; Huo, Haili: A novel fast and robust binary affine invariant descriptor for image matching (2014)
  20. Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie: A novel biologically inspired local feature descriptor (2014) ioport

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