DOI QR코드

DOI QR Code

PPD: A Robust Low-computation Local Descriptor for Mobile Image Retrieval

  • Liu, Congxin (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University) ;
  • Yang, Jie (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University) ;
  • Feng, Deying (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
  • 투고 : 2010.02.02
  • 심사 : 2010.06.04
  • 발행 : 2010.06.30

초록

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

키워드

참고문헌

  1. K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points," in Proc. IEEE International Conference on Computer Vision, vol. 1, pp. 525-531, 2001.
  2. J. Sivic and A. Zisserman, "Video Google: A text retrieval approach to object matching in videos," in Proc. IEEE International Conference on Computer Vision, Oct, 2003.
  3. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object retrieval with large vocabularies and fast spatial matching," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2007.
  4. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local features and kernels for classification of texture and object categories: a comprehensive study," Int. J. Comput. Vision, vol. 73, no. 2, pp. 213-238, 2007. https://doi.org/10.1007/s11263-006-9794-4
  5. K. Mikolajczyk and C. Schmid, "Scale & affine invariant interest point detectors," Int. J. Comput. Vision, vol. 60, no. 1, pp. 63-86, 2004. https://doi.org/10.1023/B:VISI.0000027790.02288.f2
  6. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision, vol. 60, no. 2, pp. 91-110, 2004.
  7. T. Lindeberg, "Feature Detection with Automatic Scale Selection," Int. J. Comput. Vision, vol. 30, no. 2, pp. 79-116, 1998. https://doi.org/10.1023/A:1008045108935
  8. J. Koenderink and A. J. van Doorn, "Representation of local geometry in the visual system," Biological Cybernetics, vol. 55, pp. 367-375, 1987. https://doi.org/10.1007/BF00318371
  9. W. Freeman and E. Adelson, "The design and use of steerable filters," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 9, pp. 891-906, 1991. https://doi.org/10.1109/34.93808
  10. F. Schaffalitzky and A. Zisserman, "Multi-view matching for unordered image sets," in Proc. European Conference on Computer Vision, pp. 414-431, 2002.
  11. L.J.V. Gool, T. Moons, and D. Ungureanu, "Affine/photometric invariants for planar intensity patterns," in Proc. European Conference on Computer Vision, pp. 642-651, 1996.
  12. S. Lazebnik, C. Schmid, and J. Ponce, "Sparse Texture Representation Using Affine-Invariant Neighborhoods," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2003.
  13. S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 509-522, 2002. https://doi.org/10.1109/34.993558
  14. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615-1630, 2005.
  15. P. Moreels and P. Perona, "Evaluation of features detectors and descriptors based on 3D objects," in Proc. IEEE International Conference on Computer Vision, vol. 1, pp. 800-807, 2005.
  16. Matthew Toews and William Wells III, "SIFT-Rank: Ordinal Description for Invariant Feature Correspondence," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2009.
  17. Y. Ke and R. Sukthankar, "PCA-SIFT: a more distinctive representation for local image descriptors," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2004.
  18. E. Tola, V. Lepetit, and P. Fua, "A Fast Local Descriptor for Dense Matching," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2008.
  19. Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen, Xilin Chen and Wen Gao, "WLD: A Robust Local Image Descriptor," IEEE Trans. Pattern Anal. Mach. Intell., 2009.
  20. Yoon-Sik Tak and Eenjun Hwang, "Pruning and Matching Scheme for Rotation Invariant Leaf Image Retrieval," KSII Transactions on Internet and Information Systems, vol. 2, no. 6, 2008.
  21. H. Bay, T. Tuytelaars, and L.V. Gool, "SURF: speeded up robust features," Computer Vision and Image Understanding, pp. 346-359, 2008.
  22. M. Heikkilä, M. Pietikäinen and C. Schmid, "Description of Interest Regions with Local Binary Patterns," Pattern Recognition, vol. 42, no. 3, pp. 425-436, 2009. https://doi.org/10.1016/j.patcog.2008.08.014
  23. Chun-Rong Huang, Chu-Song Chen, and Pau-Choo Chung, "Contrast context histogram – An efficient discriminating local descriptor for object recognition and image matching," Pattern Recognition, vol. 42, no. 3, pp. 425-436, 2008.
  24. http://www.robots.ox.ac.uk/~vgg/research/affine/
  25. J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust Wide Baseline Stereo from Maximally Stable Extremal Regions," IVC, September 10, pp. 761-767, 2004.
  26. T. Tuytelaars and L.V. Gool, "Matching widely separated views based on affine invariant regions," Int. J. Comput. Vision, vol. 59, no. 1, pp. 61-85, 2004. https://doi.org/10.1023/B:VISI.0000020671.28016.e8
  27. Timor Kadir, Andrew Zisserman, and Michael Brady, "An affine invariant salient region detector," in Proc. European Conference on Computer Vision, 2004.
  28. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L.V. Gool, "A comparison of affine region detectors," Int. J. Comput. Vision, vol. 65, no. 1/2, pp. 43-72, 2005. https://doi.org/10.1007/s11263-005-3848-x
  29. http://www.vlfeat.org/~vedaldi/code/sift.html
  30. X. Hou and L. Zhang, "Saliency detection: a spectral residual approach," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2007.

피인용 문헌

  1. MEGH: A New Affine Invariant Descriptor vol.7, pp.7, 2010, https://doi.org/10.3837/tiis.2013.07.010
  2. An Image Retrieving Scheme Using Salient Features and Annotation Watermarking vol.8, pp.1, 2010, https://doi.org/10.3837/tiis.2014.01.013