Feature-driven Emergence of Model Graphs for Object Recognition and Categorization

Abstract

An important requirement for the expression of cognitive structures
  is the ability to form mental objects by rapidly binding together
  constituent parts.  In this sense, one may conceive the brain's data
  structure to have the form of graphs whose nodes are labeled with
  elementary features. These provide a versatile data format with the
  additional ability to render the structure of any mental object.
  Because of the multitude of possible object variations the graphs
  are required to be dynamic. Upon presentation of an image a
  so-called model graph should rapidly emerge by binding together
  memorized subgraphs derived from earlier learning examples driven by the
  image features. In this model, the richness and flexibility of the
  mind is made possible by a combinatorical game of immense
  complexity. Consequently, the emergence of model graphs is a
  laborious task which, in computer vision, has most often been
  disregarded in favor of employing model graphs tailored to specific
  object categories like, for instance, faces in frontal pose.
  Recognition or categorization of arbitrary objects, however, demands
  dynamic graphs.

  In this work we propose a form of graph dynamics, which proceeds in
  two steps.  In the first step component classifiers, which decide
  whether a feature is present in an image, are learned from training
  images.  For processing arbitrary objects, features are small
  localized grid graphs, so-called parquet graphs, whose nodes are
  attributed with Gabor amplitudes.  Through combination of these
  classifiers into a linear discriminant that conforms to Linsker's
  infomax principle a weighted majority voting scheme is implemented.
  It allows for preselection of salient learning examples, so-called
  model candidates, and likewise for preselection of categories the
  object in the presented image supposably belongs to.  Each model
  candidate is verified in a second step using a variant of elastic
  graph matching, a standard correspondence-based technique for face
  and object recognition. To further differentiate between model
  candidates with similar features it is asserted that the features be
  in similar spatial arrangement for the model to be selected. Model
  graphs are constructed dynamically by assembling model features into
  larger graphs according to their spatial arrangement. From the
  viewpoint of pattern recognition, the presented technique is a
  combination of a discriminative (feature-based) and a generative
  (correspondence-based) classifier while the majority voting scheme
  implemented in the feature-based part is an extension of existing
  multiple feature subset methods.

  We report the results of experiments on standard databases for
  object recognition and categorization. The method achieved high
  recognition rates on identity, object category, pose, and
  illumination type.  Unlike many other models the presented
  technique can also cope with varying background, multiple objects,
  and partial occlusion.