Page 236 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems

Notes efforts may in fact be influenced by knowledge of how these are solved in nature, both in the
algorithms we employ and in the design of special-purpose hardware. Feature can be defined as
any distinctive aspect, quality or characteristic which, may be symbolic (i.e., color) or numeric
(i.e., height). The combination of d features is represented as a d-dimensional column vector
called a feature vector. The d-dimensional space defined by the feature vector is called feature
space. Objects are represented as points in feature space. This representation is called a scatter
plot.
Pattern is defined as composite of features that are characteristic of an individual. In classification,
a pattern is a pair of variables {x,w} where x is a collection of observations or features (feature
vector) and w is the concept behind the observation (label). The quality of a feature vector is
related to its ability to discriminate examples from different classes.

Did u know? Here are two fundamental approaches for implementing a pattern recognition
system: statistical and structural. Each approach employs different techniques to implement
the description and classification tasks. Hybrid approaches, sometimes referred to as a
unified approach to pattern recognition, combine both statistical and structural techniques
within a pattern recognition system.
Statistical pattern recognition draws from established concepts in statistical decision theory to
discriminate among data from different groups based upon quantitative features of the data.
There are a wide variety of statistical techniques that can be used within the description task for
feature extraction, ranging from simple descriptive statistics to complex transformations.

Examples: Of statistical feature extraction techniques include mean and standard deviation
computations, frequency count summarizations, Karhunen-Lóeve transformations, Fourier
transformations, wavelet transformations, and Hough transformations.
The quantitative features extracted from each object for statistical pattern recognition are
organized into a fixed length feature vector where the meaning associated with each feature is
determined by its position within the vector (i.e., the first feature describes a particular
characteristic of the data, the second feature describes another characteristic, and so on). The
collection of feature vectors generated by the description task are passed to the classification
task. Statistical techniques used as classifiers within the classification task include those based
on similarity (e.g., template matching, k-nearest neighbor), probability (e.g., Bayes rule),
boundaries (e.g., decision trees, neural networks), and clustering (e.g., k-means, hierarchical).
The quantitative nature of statistical pattern recognition makes it difficult to discriminate (observe
a difference) among groups based on the morphological (i.e., shape based or structural)
subpatterns and their interrelationships embedded within the data. This limitation provided
the impetus for the development of a structural approach to pattern recognition that is supported
by psychological evidence pertaining to the functioning of human perception and cognition.
Object recognition in humans has been demonstrated to involve mental representations of
explicit, structure-oriented characteristics of objects, and human classification decisions have
been shown to be made on the basis of the degree of similarity between the extracted features
and those of a prototype developed for each group. For instance, the recognition by components
theory explains the process of pattern recognition in humans: (1) the object is segmented into
separate regions according to edges defined by differences in surface characteristics (e.g.,
luminance, texture, and color), (2) each segmented region is approximated by a simple geometric
shape, and (3) the object is identified based upon the similarity in composition between the
geometric representation of the object and the central tendency of each group. This theorized
functioning of human perception and cognition serves as the foundation for the structural
approach to pattern recognition.

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