Intersection distance for object recognition

Object recognition is a very important task in the field of computer vision. We present a new method for object recognition. The image content is described by the image gradient. Then, the intersection distance is proposed to measure the similarities of the images of different objects. Our method demonstrates good performances on three face data sets.

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

[2]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[3]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[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]  Su Yang,et al.  Image matching based on orientation-magnitude histograms and global consistency , 2012, Pattern Recognit..

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

[7]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[8]  Bernd Girod,et al.  Compressed Histogram of Gradients: A Low-Bitrate Descriptor , 2011, International Journal of Computer Vision.