Visual Category Learning based on Probabilistic Latent Component Models with Semi-supervised Labeling

DOI: 10.5176/2251-2179_ATAI20

Authors: Masayasu Atsumi

Abstract:

This paper proposes a learning method of object and scene categories based on probabilistic latent component models in conjunction with semi-supervised object class labeling. In this method, a set of object segments extracted from scene images of each scene category is firstly clustered by the probabilistic latent component analysis with the variable number of classes, next the probabilistic latent component tree is generated as a classification tree of all the object classes of all the scene categories, and then object classes are incrementally labeled by propagating prior scene category labels and posterior object category labels given to representative object instances of some object classes as teaching signals. Through experiments by using images of plural categories in an image database, it is shown that the method works effectively in learning a labeled object category tree and object category composition of scene categories for object and scene recognition.

Keywords: learning; categorization; labeling; computer vision