Object Detection Based on Several Samples with Trained Hough Spaces

  • Pei Xu
  • Mao Ye
  • Min Fu
  • Xudong Li
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


A new method is proposed to handle several samples object detection case. Firstly, a new feature at each pixel, named Locally Adaptive Steering (LAS), is derived which contains the local contour and gradient variation information. Then, the cell sizes of Hough space are trained based on several samples, which represent the tolerance of appearance at each pixel location, where the Hough space is constructed by the image coordinates and the ranges of the feature values at the corresponding positions. Next, one sample image is randomly chosen as the query image and the patches with the same size of the query image are taken from the target image by sliding window. The similarities of these matching are based on the posterior decision rule in Hough space and the histogram distance. In the end, the mean shift method is applied to the similarity map to localize the instances of the object.


Locally Adaptive Steering feature Hough space histogram distance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV (2004)Google Scholar
  2. 2.
    Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)Google Scholar
  3. 3.
    Zhang, J., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision 73 (2007)Google Scholar
  4. 4.
    Zhang, Y., Chen, T.: Weakly Supevised Object Recognition and Localization with Invariant High Order Features. In: BMVC (2010)Google Scholar
  5. 5.
    Vijayanarasimhan, S., Grauman, K.: Efficient Region Search for Object Detection. In: CVPR (2011)Google Scholar
  6. 6.
    Lampert, C.H., Blaschko, M.B., Hofmann, T.: Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)Google Scholar
  7. 7.
    Lehmann, A., Leibe, B., van Gool, L.: Feature-Centric Efficient Subwindow Search. In: ICCV (2009)Google Scholar
  8. 8.
    Yao, B., Fei-Fei, L.: Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities. In: CVPR (2010)Google Scholar
  9. 9.
    Shechtman, E., Irani, M.: Matching Local Self-Similarities across Images and Videos. In: CVPR (2007)Google Scholar
  10. 10.
    Kervrann, C., Bourlanger, J.: Optimal Spatial Adaptation for Patch-Based Image Denoising. IEEE Transactions on Image Processing (2006)Google Scholar
  11. 11.
    Fei-Fei, L., Fergus, R., Perona, P.: One-Shot Learning of Object Categories. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)Google Scholar
  12. 12.
    Seo, H.J., Milanfar, P.: Training-free, Generic Object Detection Using Locally Adaptive Regression Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)Google Scholar
  13. 13.
    Takeda, H., Farsiu, S.: Kernel Regression for Image Processing and Reconstruction. IEEE Transactions on Image Processing (2007)Google Scholar
  14. 14.
    Comaniciu, D., Meer, P.: Mean Shift Analysis and Applications. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision (1999)Google Scholar
  15. 15.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  16. 16.
    S. Agarwal, A. Awan, and D. Roth. Learning to Detect Objects in Images via a Sparse, Part-Based Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004).Google Scholar
  17. 17.
    Kapoor, A., Winn, J.M.: Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 302–315. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pei Xu
    • 1
  • Mao Ye
    • 1
  • Min Fu
    • 1
  • Xudong Li
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations