Abstract
In this paper, we propose a method for extracting image features which utilizes 2nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 878–885 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Lin, Y.Y., Liu, T.L., Fuh, C.S.: Local ensemble kernel learning for object category recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Smeulders, A.W., Worring, M., Sntini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)
Boiman, O., Irani, M.: Detecting irregularities in images and in video. In: International Conference on Computer Vision, pp. 462–469 (2005)
Belongie, S., Malik, J., Puzicha, J.: Matching shapes. In: International Conference on Computer Vision, pp. 454–461 (2001)
Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2007)
Laptev, I., Lindeberg, T.: Space-time interest points. In: International Conference on Computer Vision, pp. 432–439 (2003)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International journal of computer vision 73, 213–238 (2007)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: International Conference on Computer Vision, pp. 1–8 (2007)
Lowe, D.: Distinctive image features from scale invariant features. International Journal of Compuater Vision 60, 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 20–25 (2005)
Rautkorpi, R., Iivarinen, J.: A novel shape feature for image classification and retrieval. In: International Conference on Image Analysis and Recognition, pp. 753–760 (2004)
Otsu, N., Kurita, T.: A new scheme for practical flexible and intelligent vision systems. In: IAPR Workshop on Computer Vision (1988)
Russ, J. (ed.): The Image Processing Handbook. CRC Press, Boca Raton (1995)
Winder, S., Brown, M.: Learning local image descriptors. In: Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Vapnik, V. (ed.): Statistical Learning Theory. Wiley, Chichester (1998)
Freeman, W., Adelson, E.: The design and use of steerable filters. Pattern Analysis and Machine Intelligence 13, 891–906 (1991)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. Pattern Analysis and Machine Intelligence 23, 349–361 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kobayashi, T., Otsu, N. (2008). Image Feature Extraction Using Gradient Local Auto-Correlations. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-88682-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88681-5
Online ISBN: 978-3-540-88682-2
eBook Packages: Computer ScienceComputer Science (R0)