Pascal Visual Object Classes Challenge Results

The goal of this challenge is to recognize objects from a number of visual object classes in images of realistic scenes. It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The object classes are: motorbikes, bicycles, people and cars. Twelve participants entered the challenge. A full description of the challenge including software and image sets is available on the web page http://www.pascal-network.org/challenges/VOC/voc/index.html.

[1]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[2]  Erkki Oja,et al.  PicSOM-self-organizing image retrieval with MPEG-7 content descriptors , 2002, IEEE Trans. Neural Networks.

[3]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[4]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

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

[7]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[8]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[9]  Tony Lindeberg,et al.  Direct computation of shape cues using scale-adapted spatial derivative operators , 1996, International Journal of Computer Vision.

[10]  Hermann Ney,et al.  Discriminative training for object recognition using image patches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).