Bernal S D_2010.pdf - University of Plymouth

2.1. OBJECT RECOGNITION
response from the feature of interest from irrelevant background activity, increasing the recog­
nition robustness to translations, scaling and clutter. I'unhermore, the Neocognilron places a
stronger focus on pattern recognition and less emphasis on capturing the anatomical and physi­
ological constraints imposed by the visual system.
2.1.2.3 Fmgment-bascd hierarchies
llllman (2007) proposes representing objecls within a class as a hierarchy of common image
fragments. These fragments are extracted from a training set of images based on criteria which
maximize the mutual information of fragments, then used as building blocks for a variety of
objects belonging to a common class. The fragments are then divided into different types within
each class of object, e.g. eyes, nose, mouth etc. for face recognition. During classilication, the
algorithm then selects the fragment of each type closest to the visual input following a bollom-
up approach. Kvidence from all detected fragments is combined probabilistically to reach a
final decision. By using overlapping features with different sizes and spatial resolutions, the
model is able to achieve a certain degree of position invariance. Later versions of the model
also include lop-down segmentation processes, which are beyond the scope of this chapter.
The fragment-based method introduces several novelties in relation to previous feature-based
approaches: object fragments are class specific, are organized into fragment types with vary­
ing degrees of complexity, and employ new learning methods to extract the most informative
fragments. However, the model is derived from computer vision approaches, hence relating
to the visual system only al a very abstract level. Some basic principles of hierarchical ob­
ject recognition are captured and the author puts forward psychophysical and physiological
evidence suggestive of class specific features emerging in the visual system during category
learning. Feature tuning is not based on physiological data (e.g. VI features arc richer than the
standard model suggests), connectivity is not derived from cortical anatomy but from the image
fragmcnialion process, and a biologically plausible implementation of the model operations has
not been demonstrated.
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