论文信息 - Local intensity distribution descriptor for object detection

Local intensity distribution descriptor for object detection

A novel local intensity distribution (LID) descriptor for object detection is proposed. By capturing the distribution of local intensity differences effectively, the LID descriptor is insensitive to illumination changes while being more compact and discriminative compared with the popular local binary pattern. Two LID descriptors can be efficiently compared using the Kullback-Leibler distance. The efficacy and efficiency of the proposed descriptor have been verified in the task of detecting humans from static images.

Wanqing Li | Philip Ogunbona | Duc Thanh Nguyen

[1] R. A. Leibler,et al. On Information and Sufficiency , 1951 .

[2] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[3] Tomaso A. Poggio,et al. Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4] Xiaoyang Tan,et al. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[5] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6] Wen Gao,et al. Object detection using spatial histogram features , 2006, Image Vis. Comput..

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