  Object recognition has become a crucial part of machine learning and computer
vision recently. The current approach to object recognition involves Deep
Learning and uses Convolutional Neural Networks to learn the pixel patterns of
the objects implicitly through backpropagation. However, CNNs require thousands
of examples in order to generalize successfully and often require heavy
computing resources for training. This is considered rather sluggish when
compared to the human ability to generalize and learn new categories given just
a single example. Additionally, CNNs make it difficult to explicitly
programmatically modify or intuitively interpret their learned representations.
  We propose a computational model that can successfully learn an object
category from as few as one example and allows its learning style to be
tailored explicitly to a scenario. Our model decomposes each image into two
attributes: shape and color distribution. We then use a Bayesian criterion to
probabilistically determine the likelihood of each category. The model takes
each factor into account based on importance and calculates the conditional
probability of the object belonging to each learned category. Our model is not
only applicable to visual scenarios, but can also be implemented in a broader
and more practical scope of situations such as Natural Language Processing as
well as other places where it is possible to retrieve and construct individual
attributes. Because the only condition our model presents is the ability to
retrieve and construct individual attributes such as shape and color, it can be
applied to essentially any class of visual objects.
