CS Faculty Candidate Colloquium
Friday **Special Time and Location** January 11 11:00 - 11:50 AM Kelley 1007 Dr. Sinisa Todorovic EECS Colloquium: Computer Science Faculty Candidate Department of Electrical and Computer Engineering, Beckman Institute University of Illinois at Urbana-Champaign WHAT DO THOSE IMAGES HAVE IN COMMON? This talk is about discovering and modeling previously unspecified, recurring themes in a given set of arbitrary images. Specifically, given a set of images containing frequent occurrences of objects from multiple categories, the goal is to learn a compact model that captures the canonical image-space properties of the categories as well as their relationships, for the purposes of later recognizing and extracting any occurrences in new images. The object categories are unknown a priori. Whether and where instances of any categories appear in a specific image is also not known. This problem is challenging, since it involves the following unanswered questions. What is an object category? To what extent human supervision is necessary to communicate the nature of object categories to a computer? What image properties should be used to define an efficient multicategory representation? We will examine a methodology, developed during my postdoctoral work in the Beckman Institute at the University of Illinois Urbana-Champaign, which addresses these questions when objects are characterized in 2D. A category is defined as a set of 2D objects (i.e., subimages) sharing photometric, geometric and topological properties of their constituent regions (e.g., color, area, shape, recursive embedding of regions). Each image is represented by a segmentation tree whose nodes correspond to image regions at all natural scales present, and edges between tree nodes capture the embedding of small regions within larger ones. The nodes contain the associated region properties. The presence of any categories in the image set is then reflected in the frequent occurrence of similar subtrees (i.e., 2D objects) within the image segmentation trees. Our methodology is designed to: (1) match image trees to find similar subtrees; (2) discover categories by clustering similar subtrees, and use the properties of each cluster to learn the model of the associated category; and (3) learn the grammar of the discovered categories that compactly captures their recursive definitions in terms of other simpler (sub)categories and their relationships (e.g., co-occurrence, and sharing of simple categories by more complex ones). When a new image is encountered, its segmentation tree is matched against the category grammar to simultaneously detect, recognize and segment all occurrences of the learned categories. This matching also provides a semantic explanation of object recognition in terms of the identified subcategories (i.e., object parts) along with their spatial relationships. The aforementioned methodology can also be used for detecting recurring image themes of more general kind. An example is that of identifying and extracting the stochastically repeating, elementary parts of visual texture, commonly called as texture elements or texels (e.g., waterlilies on the water surface, a housing development, crowd of people). Biography Sinisa Todorovic received the joint B.S./M.S. degree with honors in electrical engineering from the University of Belgrade, Serbia, in 1994. >From 1994 until 2001, he worked as a software engineer in the communications industry. He received his M.S. and Ph.D. degrees in electrical and computer engineering at the University of Florida, Gainesville, in 2002, and 2005, respectively. Since 2005, he holds the position of Postdoctoral Research Associate in the Beckman Institute at the University of Illinois Urbana-Champaign, where he collaborates with Prof. Narendra Ahuja. Sinisa's main research interests concern computer vision and machine learning, with current focus on unsupervised extraction and representation of spatial structures recurring in images and video. He is the recipient of Jack Neubauer Best Paper Award 2004 for a publication in IEEE Trans. Vehicular Technology, and Outstanding Reviewer Award at the International Conf. on Computer Vision (ICCV) 2007. He serves as Associate Editor of Advances in Multimedia.
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