Inducing Discrimination In Biologically Inspired Models Of Visual Scene Recognition
Tayyaba Azim, Mahesan Niranjan

To enhance the understanding of human perception and
mimic it into an artificial system, several types of graphical
models have been proposed that emulate the functionality
of neurons in biological neural networks. In this work, we
investigate the discriminatory power of two such probabilistic
models of vision: a multivariate Gaussian model [1] and a
restricted Boltzmann machine [2], both widely used to solve
classification problems in computer vision. We quantify the
generative ability of these models on standard benchmark
data sets and show that neither approach on their own is
powerful enough to carry out vision tasks because of the
very low discrimination they achieve. There is clearly a need
for inducing discrimination by a mechanism that exploits
these generative models. We show that the Fisher kernels [3]
derived from both the Gaussian and restricted Boltzmann
machine can significantly improve the classification performance
on benchmark tasks while maintaining the biological
plausibility of its implementation [4].