Using Linking Features in Learning Non-parametric Part Models

  • Leonid Karlinsky
  • Shimon Ullman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)


We present an approach to the detection of parts of highly deformable objects, such as the human body. Instead of using kinematic constraints on relative angles used by most existing approaches for modeling part-to-part relations, we learn and use special observed ‘linking’ features that support particular pairwise part configurations. In addition to modeling the appearance of individual parts, the current approach adds modeling of the appearance of part-linking, which is shown to provide useful information. For example, configurations of the lower and upper arms are supported by observing corresponding appearances of the elbow or other relevant features. The proposed model combines the support from all the linking features observed in a test image to infer the most likely joint configuration of all the parts of interest. The approach is trained using images with annotated parts, but no a-priori known part connections or connection parameters are assumed, and the linking features are discovered automatically during training. We evaluate the performance of the proposed approach on two challenging human body parts detection datasets, and obtain performance comparable, and in some cases superior, to the state-of-the-art. In addition, the approach generality is shown by applying it without modification to part detection on datasets of animal parts and of facial fiducial points.


linking features parts detection FLPM model 


  1. 1.
    Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV (2005)Google Scholar
  2. 2.
    Ramanan, D.: Learning to parse images of articulated objects. In: NIPS (2006)Google Scholar
  3. 3.
    Eichner, M., Ferrari, V.: Better appearance models for pictorial structures. In: BMVC (2009)Google Scholar
  4. 4.
    Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation. In: CVPR (2009)Google Scholar
  5. 5.
    Wang, Y., Mori, G.: Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 710–724. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Jiang, H., Martin, D.R.: Global pose estimation using non-tree models. In: CVPR (2008)Google Scholar
  7. 7.
    Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: CVPR (2010)Google Scholar
  8. 8.
    Sapp, B., Jordan, C., Taskar, B.: Adaptive pose priors for pictorial structures. In: CVPR (2010)Google Scholar
  9. 9.
    Sapp, B., Toshev, A., Taskar, B.: Cascaded Models for Articulated Pose Estimation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 406–420. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Yang, Y., Ramanan, D.: Articulated pose estimation using flexible mixtures of parts. In: CVPR (2011)Google Scholar
  11. 11.
    Ioffe, S., Forsyth, D.: Human tracking with mixtures of trees. In: ICCV (2001)Google Scholar
  12. 12.
    Sapp, B., Weiss, D., Taskar, B.: Parsing human motion with stretchable models. In: CVPR (2011)Google Scholar
  13. 13.
    Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3d human pose annotations. In: ICCV (2009)Google Scholar
  14. 14.
    Karlinsky, L., Dinerstein, M., Harari, D., Ullman, S.: The chains model for detecting parts by their context. In: CVPR (2010)Google Scholar
  15. 15.
    Martinez, A., Benavente, R.: The ar face database. CVC Technical Report num. 24 (1998)Google Scholar
  16. 16.
    Ding, L., Martinez, A.M.: Features versus context: An approach for precise and detailed detection and delineation of faces and facial features. PAMI (2010)Google Scholar
  17. 17.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV (2004)Google Scholar
  18. 18.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across different scenes and its applications. PAMI (2010)Google Scholar
  19. 19.
    Mount, D., Arya, S.: Ann: A library for approximate nearest neighbor searching. CGC (1997)Google Scholar
  20. 20.
    Duda, R., Hart, P.: Pattern classification and scene analysis. Wiley (1973)Google Scholar
  21. 21.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leonid Karlinsky
    • 1
  • Shimon Ullman
    • 1
  1. 1.Weizmann Institute of ScienceRehovotIsrael

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