A lightweight inference method for image classification
John Mark Agosta, Preeti J. Pillai · 2013
We demonstrate a two phase classifica-tion method, first of individual pixels, then of fixed regions of pixels for scene classification—the task of assigning posteri-ors that characterize an entire image. This can be realized with a probabilistic graphical model (PGM), without the characteristic seg-mentation and aggregation tasks characteris-tic of visual object recognition. Instead the spatial aspects of the reasoning task are de-termined separately by a segmented partition of the image that is fixed before feature ex-traction. The partition generates histograms of pixel classifications treated as virtual evi-dence to the PGM. We implement a sampling method to learn the PGM using virtual ev-idence. Tests on a provisional dataset show good (+70%) classification accuracy among most all classes. 1