Statistical color models with application to skin detection
International Journal of Computer Vision
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Skin and non-skin probability approximation based on discriminative tree distribution
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Scalable logo recognition in real-world images
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Towards automatic object annotations from global image labels
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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This paper presents a method for creating a discriminative color model for a given object class based on color occurrence statistics. A discriminative color model can be used to classify individual pixels of images with regards to whether they may belong to the wanted object. However, in contrast to existing approaches, we do not exploit pixel-wise object annotations but only global negative and positive image labels. Therefore our approach requires significantly less manual effort. We quantitatively evaluate the performance of our approach on two publicly available datasets and compare it to a baseline approach, which utilizes pixel annotations. The experimental results show that our approach is on par with pixel-wise approaches although requiring only a single global image label.