CMU talk: Unsupervised Detection of Regions of Interest Using Iterative Link Analysis

CMU VASC Seminar
Monday, November 30, 2009

Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
Gunhee Kim
Ph.D. Student, Computer Science Department

Abstract:

This work is a joint project with Antonio Torralba during my visit to MIT and will be presented as a poster at the upcoming NIPS 2009 Conference.

This talk will discuss a fast and scalable alternating optimization technique to detect regions of interest (ROIs) in cluttered Web images without labels. The proposed approach discovers highly probable regions of object instances by iteratively repeating the following two functions: (1) choose the exemplar set (i.e. a small number of highly ranked reference ROIs) across the dataset and (2) refine the ROIs of each image with respect to the exemplar set. These two subproblems are formulated as ranking in two different similarity networks of ROI hypotheses by link analysis. The experiments with the PASCAL 06 dataset show that our unsupervised localization performance is better than one of the state-of-the-art techniques and comparable to supervised methods. Also, we test the scalability of our approach with five objects in a Flickr dataset consisting of more than 200K images.

Bio: Gunhee Kim is a Ph.D. student in CMU's Computer Science Department advised by Takeo Kanade. He received his master's degree under the supervision of Martial Hebert in 2008 from the Robotics Institute at CMU. His research interests are computer vision, machine learning, data mining, and biomedical imaging.