Event Location: NSH 1507
Bio: Praveen Srinivasan is a 5th-year PhD student in the GRASP Lab at the
University of Pennsylvania, and is advised by Prof. Jianbo Shi. His
research interests in computer vision include object recognition, video
understanding, perceptual grouping, and human pose estimation. He has
published his work in conferences such as CVPR and NIPS, and was
partially supported by an NSF Graduate Fellowship.

Abstract: We present an object recognition system that locates an object,
identifies its parts, and segments out its contours. A key distinction
of our approach is that we use long, salient, bottom-up image contours
to learn object shape, and to achieve object detection with the learned
shape. Most learning methods rely on one-to-one matching of contours to
a model. However, bottom-up image contours often fragment unpredictably.
We resolve this difficulty by using many-to-one matching of image
contours to a model. To learn a descriptive object shape model, we
combine bottom-up contours from a few representative images. The goal is
to allow most of the contours in the training images to be many-to-one
matched to the model. For detection, our challenges are inferring the
object contours and part locations, in addition to object location.
Because the locations of object parts and matches of contours are not
annotated, they appear as latent variables during training. We use the
latent SVM learning formulation to discriminatively tune the many-to-one
matching score using the max-margin criterion. We evaluate on the
challenging ETHZ shape categories dataset and outperform all existing
methods. We also demonstrate recognition of articulated objects
(humans). This is joint work with Weiyu Zhang, Qihui Zhu and Jianbo Shi.