20. F. Schroff, A. Criminisi, A. Zisserman: Single-Histogram Class Models for Image Segmentation
Histograms of visual words (or textons) have proved effective in tasks such as image classification and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nearest neighbour search over the exemplars.
We introduce three novelties on this approach: (i) we show that new
compact single histogram models estimated optimally from the entire
training set achieve an equal classification accuracy.
The benefit of the single histograms is that they are much more
efficient both in terms of memory and computational resources; (ii) we
show that bag of visual words histograms can provide an accurate
pixel-wise segmentation of an image into object class regions. In this
manner the compact models of visual object classes give simultaneous
segmentation and recognition of image regions; and (iii) we show that
a mixture of classes model can provide a confidence in the
segmentation, allowing a reject option to be employed.
The approach is evaluated on the MSRC database [5] and it is shown
that accuracy equals previous publications on this database, but with
simpler class models.