Bag-of-features approaches for combined classification of laser scanning microscopy and spectroscopy data sets

The Bag-of-Features (BoF) paradigm represents a solid solution for the automated classification of digital images. Several BoF approaches for classification of microscopy data have been reported in the past decade, but their number is very low considering the potential that BoF methods hold with respect to this subject. In this contribution we discuss strategies for using BoF architectures for the automated classification of 1D and 2D data sets collected using Laser Scanning Microscopy techniques.