Foundations of Pattern Recognition (I)

Pattern recognition is a fundamental ability shared by humans and machines alike. While humans effortlessly interpret shapes and letters on paper, computers view them as sequences of binary digits stored in memory. Extracting meaningful information from these binary sequences and transforming it into a format suitable for further processing is the essence of pattern recognition. Patterns can range from printed letters and handwriting to technical drawings and maps.

The term “pattern” derives from “patron,” originally denoting a perfect example to emulate. In psychology, pattern recognition refers to converting sensory signals into recognizable perceptions. It involves identifying ideal characteristics that define a class to which an object belongs. Unknown objects are compared against these ideal characteristics, determining their class membership based on the degree of similarity.

Example: Recognizing a geometric triangle image involves:
– Defining ideal triangle characteristics: geometric shape, two-dimensional structure, three sides, three vertices, and three angles.
– Comparing these with the features of an unknown shape to identify it as a triangle if they match.

A key question is how to define the ideal concept. This can be achieved deductively (innate concept) or inductively (through observation and learning from non-ideal examples). Learning involves two types:
– Supervised learning: Using labeled samples to learn class representations.
– Unsupervised learning: Utilizing unlabeled samples to discern patterns independently.

Learning to classify objects into predefined classes is pivotal in both human and artificial intelligence research. It emphasizes identifying essential characteristics that determine class membership, crucial for directing attention and making informed decisions. Generalization plays a critical role by isolating these essential traits, enabling the classification of new objects into known classes.

Example: Recognizing a leopard involves focusing on spotting rather than eye color, demonstrating the importance of distinguishing essential traits for accurate classification, especially in critical situations.

Thus, while humans excel at recognizing complex patterns, challenges like rapid multiplication of large numbers highlight distinct cognitive strengths and limitations.