Hierarchical image-region labeling via structured learning
Julian McAuley, Teofilo Campos, Gabriela Csurka and Florent Perronin
In: BMVC 2009, 7-10 Sep 2009, London, England.
We present a graphical model which encodes a series of hierarchical constraints for classifying image regions at multiple scales. We show that inference in this model can be performed efficiently and exactly, rendering it amenable to structured learning. Rather than using feature vectors derived from images themselves, our model is parametrised using the outputs of a series of first-order classifiers. Thus our model learns which classifiers are useful at different scales, and also the relationships between classifiers at different scales. We present promising results on the VOC2007 and VOC2008 datasets.