[VASC Seminar Series]Unsupervised Learning of Categories Appearing in Images

Date: Monday, Oct 8
Title: Unsupervised Learning of Categories Appearing in Images
Speaker: Sinisa Todorovic, UIUC

Abstract:
This talk is about solving the following problem: given a set of images containing frequent occurrences of multiple object categories, learn a compact, multi-category representation that encodes the models of these categories and their inter-category relationships, for the purposes of object recognition and segmentation. The categories are not defined by the user, and whether and where any instances of the categories appear in a specific image is not known. This problem is challenging as it involves the following unanswered questions. What is an object category? To which
extent human supervision is necessary to communicate the nature of object categories to a computer vision system? What is an efficient, compact representation of multiple categories, and which inter-category relationships should it capture? I will present an approach that addresses the above stated problem, wherein a category is defined as a set of 2D objects (i.e., subimages) sharing similar appearance and topological properties of their constituent regions. The approach derives from and
closely follows this definition by representing each image as a segmentation tree, whose structure captures recursive embedding of image regions in a multiscale segmentation, and whose nodes contain the associated geometric and photometric region properties. Since the presence of any categories in the image set is reflected in the occurrence of similar subtrees (i.e., 2D objects) within the image trees, the approach: (1) matches the image trees to find these similar subtrees; (2) discovers
categories by clustering similar subtrees, and uses the properties of each cluster to learn the model of the associated category; and (3) captures sharing of simpler categories among complex ones, i.e., category-subcategory relationships. The approach can also be used for addressing a less-general, subsumed problem, that of unsupervised extraction of texture elements ( i.e., texels) from a given image of 2.1D
texture, because 2.1D texture can be viewed as composed of repetitive instances of a category (e.g., waterlilies on the water surface).