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Learning a Family of Detectors via Multiplicative Kernels

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Topics in Medical Image Processing and Computational Vision

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 8))

Abstract

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.

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Notes

  1. 1.

    In this paper, all vector variables are column vectors.

  2. 2.

    available at http://cs-people.bu.edu/yq/projects/mk.html.

  3. 3.

    available at http://cs-people.bu.edu/yq/projects/mk.html.

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Acknowledgments

This paper reports work that was supported in part by the U.S. National Science Foundation under grants IIS-0705749 and IIS-0713168.

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Authors and Affiliations

  1. Ventana Medical Systems, 919 Hermosa CT, Sunnyvale, CA, 94085, USA

    Quan Yuan

  2. Department of Computer Science, Boston University, 111 Cummington Street, Boston, MA, 02215, USA

    Ashwin Thangali & Stan Sclaroff

  3. EPFL IC-CVLab, BC 309 Station 14, CH-1015, Lausanne, Switzerland

    Vitaly Ablavsky

Authors
  1. Quan Yuan
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  2. Ashwin Thangali
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  3. Vitaly Ablavsky
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  4. Stan Sclaroff
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Corresponding author

Correspondence to Quan Yuan .

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Editors and Affiliations

  1. Faculdade de Engenharia, Departamento de Engenharia Mecânica, Universidade do Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal

    JoĂŁo Manuel R.S. Tavares

  2. Faculdade de Engenharia, Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Porto, 4200-465, Portugal

    Renato M. Natal Jorge

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Yuan, Q., Thangali, A., Ablavsky, V., Sclaroff, S. (2013). Learning a Family of Detectors via Multiplicative Kernels. In: Tavares, J., Natal Jorge, R. (eds) Topics in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0726-9_1

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  • DOI: https://doi.org/10.1007/978-94-007-0726-9_1

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