Unsupervised Learning for Visual Pattern Analysis

Abstract

AbstractThis chapter presents an overview of topics and major concepts in unsupervised learning for visual pattern analysis. Cluster analysis and dimensionality are two important topics in unsupervised learning. Clustering relates to the grouping of similar objects in visual perception, while dimensionality reduction is essential for the compact representation of visual patterns. In this chapter, we focus on clustering techniques, offering first a theoretical basis, then a look at some applications in visual pattern analysis. With respect to the former, we introduce both concepts and algorithms. With respect to the latter, we discuss visual perceptual grouping. In particular, the problem of image segmentation is discussed in terms of contour and region grouping. Finally, we present a brief introduction to learning visual pattern representations, which serves as a prelude to the following chapters.KeywordsCluster AlgorithmUnsupervised LearnVisual PatternPerceptual GroupingActive Contour ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.