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Computer Science @ Rochester
Tuesday, April 29, 2008
1:30 PM
Computer Studies Bldg. Room 632
Ph.D. Thesis Proposal
Nicholas Morsillo
University of Rochester
Semi-Supervised Probabilistic Models for Large-Scale Dataset Construction
Object recognition is a fundamental problem for computer vision with a wide range of practical applications. Although the field of visual recognition continues to hold great research promise, state of the art recognition systems have had limited real-world success. A primary drawback of current methods is the inability to scale with large amounts of training data and many visual categories.

Scalability is increasingly crucial as the availability of digital imagery increases. The internet hosts billions of images which can potentially serve as rich sources of training data for recognition. Our work will explore this option through the following contributions. 1. A new method for collecting large, clean image sets of specific object categories from web image search. Our approach will use metadata including web page text to reduce the need for supervision, and it will exploit properties of web images for automatic segmentation and discovery of contextual information. 2. Creation of the first large-scale image dataset with clean labels, segmentation masks, and context descriptors. 3. A novel probabilistic model for visual recognition to efficiently combine object and contextual information from large-scale datasets. We will thoroughly evaluate our approach by testing recognition performance on known datasets, using our processed web images for training.