  This article presents a model which is capable of learning and abstracting
new concepts based on comparing observations and finding the resemblance
between the observations. In the model, the new observations are compared with
the templates which have been derived from the previous experiences. In the
first stage, the objects are first represented through a geometric description
which is used for finding the object boundaries and a descriptor which is
inspired by the human visual system and then they are fed into the model. Next,
the new observations are identified through comparing them with the
previously-learned templates and are used for producing new templates. The
comparisons are made based on measures like Euclidean or correlation distance.
The new template is created by applying onion-pealing algorithm. The algorithm
consecutively uses convex hulls which are made by the points representing the
objects. If the new observation is remarkably similar to one of the observed
categories, it is no longer utilized in creating a new template. The existing
templates are used to provide a description of the new observation. This
description is provided in the templates space. Each template represents a
dimension of the feature space. The degree of the resemblance each template
bears to each object indicates the value associated with the object in that
dimension of the templates space. In this way, the description of the new
observation becomes more accurate and detailed as the time passes and the
experiences increase. We have used this model for learning and recognizing the
new polygons in the polygon space. Representing the polygons was made possible
through employing a geometric method and a method inspired by human visual
system. Various implementations of the model have been compared. The evaluation
results of the model prove its efficiency in learning and deriving new
templates.
