When: Monday, May 12, 3:30 p.m.

Where: 1507 Newell-Simon Hall

Tomasz Malisiewicz , Robotics Institute

VASC Seminar

Abstract:
Many multi-class object recognition systems focus on categorization, where the goal is to predict a novel object's category given its feature representation. In this talk, I pose the recognition problem as data association. In this setting, a novel object is explained solely in terms of a small set of exemplar objects to which it is visually similar. Inspired by the work of Frome et al., we learn separate distance functions for each exemplar; however, our distances are interpretable on an absolute scale and can be thresholded to detect the presence of an object. Our exemplars are represented as image regions and the learned distances capture the relative importance of shape, color, texture, and position features for that region. We use the distance functions to detect and segment objects in novel images by associating the bottom-up segments obtained from multiple image segmentations with the exemplar regions. We evaluate the detection and segmentation performance of our non-parametric exemplar-based system on real-world outdoor scenes from the LabelMe dataset and also show some promising qualitative image parsing results.

Tomasz Malisiewicz obtained his B.S. in Computer Science and Physics from Rensselaer Polytechnic Institute (RPI) in 2005. He has been a PhD student at Carnegie Mellon University's Robotics Institute since 2005 and is advised by Alexei A. Efros. His research interests are in computer vision and machine learning, focusing on multi-class object recognition and segmentation. Since 2006 his research has been supported by a National Science Foundation Graduate Research Fellowship.

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