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Edge Boxes: Locating Object Proposals from Edges

  • C. Lawrence Zitnick
  • Piotr Dollár
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box’s boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96% object recall at overlap threshold of 0.5 and over 75% recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.

Keywords

object proposals object detection edge detection 

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References

  1. 1.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  3. 3.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  4. 4.
    Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. PAMI 34(11) (2012)Google Scholar
  5. 5.
    Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. IJCV (2013)Google Scholar
  6. 6.
    Carreira, J., Sminchisescu, C.: Cpmc: Automatic object segmentation using constrained parametric min-cuts. PAMI 34(7) (2012)Google Scholar
  7. 7.
    Rahtu, E., Kannala, J., Blaschko, M.: Learning a category independent object detection cascade. In: ICCV (2011)Google Scholar
  8. 8.
    Manen, S., Guillaumin, M., Van Gool, L., Leuven, K.: Prime object proposals with randomized prims algorithm. In: ICCV (2013)Google Scholar
  9. 9.
    Endres, I., Hoiem, D.: Category-independent object proposals with diverse ranking. PAMI (2014)Google Scholar
  10. 10.
    Rantalankila, P., Kannala, J., Rahtu, E.: Generating object segmentation proposals using global and local search. In: CVPR (2014)Google Scholar
  11. 11.
    Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: Binarized normed gradients for objectness estimation at 300fps. In: CVPR (2014)Google Scholar
  12. 12.
    Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. In: ICCV (2013)Google Scholar
  13. 13.
    Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)Google Scholar
  14. 14.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  15. 15.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303–338 (2010)Google Scholar
  16. 16.
    Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013)Google Scholar
  17. 17.
    Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. Inc., New York, NY (1982)Google Scholar
  18. 18.
    Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Transactions Graphics 31(4) (2012)Google Scholar
  19. 19.
    Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. CoRR abs/1406.5549 (2014)Google Scholar
  20. 20.
    Deselaers, T., Alexe, B., Ferrari, V.: Localizing objects while learning their appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 452–466. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Siva, P., Xiang, T.: Weakly supervised object detector learning with model drift detection. In: ICCV (2011)Google Scholar
  22. 22.
    Gu, C., Lim, J.J., Arbeláez, P., Malik, J.: Recognition using regions. In: CVPR (2009)Google Scholar
  23. 23.
    Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: ICCV (2005)Google Scholar
  24. 24.
    Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)Google Scholar
  25. 25.
    Malisiewicz, T., Efros, A.A.: Improving spatial support for objects via multiple segmentations. In: BMVC (2007)Google Scholar
  26. 26.
    Hosang, J., Benenson, R., Schiele, B.: How good are detection proposals, really? In: BMVC (2014)Google Scholar
  27. 27.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59(2) (2004)Google Scholar
  28. 28.
    Canny, J.: A computational approach to edge detection. PAMI (6), 679–698 (1986)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • C. Lawrence Zitnick
    • 1
  • Piotr Dollár
    • 1
  1. 1.Microsoft ResearchUSA

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