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

This paper proposes a learning method of object andscene categories based on probabilistic latent component modelsin conjunction with semi-supervised object class labeling. In thismethod, a set of object segments extracted from scene images ofeach scene category is firstly clustered by the probabilistic latentcomponent analysis with the variable number of classes, next theprobabilistic latent component tree is generated as a classificationtree of all the object classes of all the scene categories, andthen object classes are incrementally labeled by propagatingprior scene category labels and posterior object category labelsgiven to representative object instances of some object classes asteaching signals. Through experiments by using images of pluralcategories in an image database, it is shown that the methodworks effectively in learning a labeled object category tree andobject category composition of scene categories and achieves highperformance for object and scene recognition