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Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Graph-based classification of multiple observation sets
We consider the problem of classification of an object given multiple
observations that possibly include different transformations. The possible
transformations of the object generally span a low-dimensional manifold in the
original signal space. We propose to take advantage of this manifold structure
for the effective classification of the object represented by the observation
set. In particular, we design a low complexity solution that is able to exploit
the properties of the data manifolds with a graph-based algorithm. Hence, we
formulate the computation of the unknown label matrix as a smoothing process on
the manifold under the constraint that all observations represent an object of
one single class. It results into a discrete optimization problem, which can be
solved by an efficient and low complexity algorithm. We demonstrate the
performance of the proposed graph-based algorithm in the classification of sets
of multiple images. Moreover, we show its high potential in video-based face
recognition, where it outperforms state-of-the-art solutions that fall short of
exploiting the manifold structure of the face image data sets.Comment: New content adde
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