Deriving a discriminative color model for a given object class from weakly labeled training data
Christian X. Ries, Rainer W. Lienhart · 2012
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.