Introducing Memory and Association Mechanism into a Biologically
  Inspired Visual Model
  A famous biologically inspired hierarchical model firstly proposed by
Riesenhuber and Poggio has been successfully applied to multiple visual
recognition tasks. The model is able to achieve a set of position- and
scale-tolerant recognition, which is a central problem in pattern recognition.
In this paper, based on some other biological experimental results, we
introduce the Memory and Association Mechanisms into the above biologically
inspired model. The main motivations of the work are (a) to mimic the active
memory and association mechanism and add the 'top down' adjustment to the above
biologically inspired hierarchical model and (b) to build up an algorithm which
can save the space and keep a good recognition performance. The new model is
also applied to object recognition processes. The primary experimental results
show that our method is efficient with much less memory requirement.
