OPTIMOL: a framework for online picture collection via incremental model learning

Authors:
Li-Jia Li;Juan Carlos Niebles;Li Fei-Fei
Affiliations:
University of Illinois;Universidad del Norte and University of Illinois;Princeton University
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

Citing 2
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Abstract

OPTIMOL (a framework for Online Picture collection via Incremental MOdel Learning) is a novel, automatic dataset collecting and model learning system for object categorization. Our algorithm mimics the human learning process in such a way that, starting from a few training examples, the more confident data you incorporate in the training data, the more reliahle models can be learnt. Our system uses the Internet as the (nearly) unlimited resource for images. The learning and image collection processes are done via an iterative and incremental scheme. The goal of this work is to use this tremendous web resource to learn robust object category models in order to detect and search for objects in real-world scenes.