Feature-centric Efficient Subwindow Search

Many object detection systems rely on linear classifiers embedded in a sliding-window scheme. Such exhaustive search involves massive computation. Efficient Subwindow Search (ESS) [11] avoids this by means of branch and bound. However, ESS makes an unfavourable memory tradeoff. Memory usage scales with both image size and overall object model size. This risks becoming prohibitive in a multiclass system. In this paper, we make the connection between sliding-window and Hough-based object detection explicit. Then, we show that the feature-centric view of the latter also nicely fits with the branch and bound paradigm, while it avoids the ESS memory tradeoff. Moreover, on-line integral image calculations are not needed. Both theoretical and quantitative comparisons with the ESS bound are provided, showing that none of this comes at the expense of performance.

[1]  Cordelia Schmid,et al.  Accurate Object Detection with Deformable Shape Models Learnt from Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Cordelia Schmid,et al.  Viewpoint-independent object class detection using 3D Feature Maps , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[6]  Takeo Kanade,et al.  Object Detection Using the Statistics of Parts , 2004, International Journal of Computer Vision.

[7]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[10]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[11]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .

[13]  M. V. Wilkes,et al.  The Art of Computer Programming, Volume 3, Sorting and Searching , 1974 .

[14]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[15]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[16]  Luc Van Gool,et al.  PRISM: PRincipled Implicit Shape Model , 2009, BMVC.

[17]  Andrew Zisserman,et al.  A Boundary-Fragment-Model for Object Detection , 2006, ECCV.

[18]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Bernt Schiele,et al.  Integrating representative and discriminant models for object category detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[20]  T. Breuel,et al.  Electronic Letters on Computer Vision and Image Analysis 6(1):44-54, 2007 Optimal Geometric Matching for Patch-Based Object Detection , 2006 .