Bernal S D_2010.pdf - University of Plymouth

3.4. EXISTING MODELS
The model computes the similarity between each low-level feature and the image at H different
ItKations. which is used as input evidence for the network (similar to dummy nodes in Bayesian
networks). A simple bottom-up sweep of the belief propagation algorithm then obtains the
probability distribution for each variable (i.e. presence/location of each fragment), including
that of the root node, which represents the class. Note, unlike conventional feedforward meth­
ods, the model computes the relative likelihoods of all cla.ss sub-hierarchies given the stimuli
(i.e. there is a graph for each class of objects), leading to multiple alternatives at each level of
the model. Later, a top-down cycle obtains the optimal value for all the object parts given the
state/location of the root/class node, correcting most of the errors made during the bottom-up
pass. This provides not only object recognition, bui a detailed interpretation of the image ai
different scales and levels of detail. The model was tested a large number of natural images
belonging to three different object classes.
Unlike most related models (Riesenhuber and Poggio 1999, George and Hawkins 2009, Murray
and Kreutz-Delgado 2007, Lewicki and Sejnowski 1997), where nodes represent locations and
states represent features, the model by lipshtein et al. (200B) uses nodes to represent features and
stales to represent locations. In essence, the network includes a fixed hierarchical representation
of ail the possible combinations of features and subfealures of a class of objects. The graph is a
singly connected tree (no loops and a single parent per node) which makes tractable the use of
belief propagation to perform exact inference.
However, the previous properties imply that features are not shared within the same object (each
feature can only be present at one given location), amongst different objects of the same class
(the graph for each class is singly connected), or within objects of different classes (there is an
independent network for each object class). This lack of overlap between features speaks for
an inefficient coding strategy, as low-level features of distinct objects are likely to be similar.
Additionally, the model is restricted to a set of informative learned fragments, which, for exam­
ple, limit its ability to explain retinotopic contour completion at an arbitrary (less informative)
object region.
A second model falling into this category is that proposed by Chikkerur et al. (2010). It uses
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