Imperfect learning for autonomous concept modeling

Ching-Yung Lin; Xiaodan Song; Gang Wu

doi:10.1117/12.588299

17 January 2005 Imperfect learning for autonomous concept modeling

Ching-Yung Lin, Xiaodan Song, Gang Wu

Proceedings Volume 5682, Storage and Retrieval Methods and Applications for Multimedia 2005; (2005) https://doi.org/10.1117/12.588299
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Most existing supervised machine learning frameworks assume there is no mistake or false interpretation on the training samples. However, this assumption may not be true in practical applications. In some cases, if human being is involved in providing training samples, there may be errors in the training set. In this paper, we study the effect of imperfect training samples on the supervised machine learning framework. We focus on the mathematical framework that describes the learnability of noisy training data. We study theorems to estimate the error bounds of generated models and the required amount of training samples. These errors are dependent on the amount of data trained and the probability of the accuracy of training data. Based on the effectiveness of learnability on imperfect annotation, we describe an autonomous learning framework, which uses cross-modality information to learn concept models. For instance, visual concept models can be trained based on the detection result of Automatic Speech Recognition, Closed Captions, or prior detection results of the same modality. Those detection results on an unsupervised training set serve as imperfect labeling for the models-to-build. A prototype system based on this learning technique has been built. Promising results have been shown on these experiments.

Citation Download Citation

Ching-Yung Lin, Xiaodan Song, and Gang Wu "Imperfect learning for autonomous concept modeling", Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005); https://doi.org/10.1117/12.588299

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Virtual colonoscopy

Machine learning

Video

Statistical modeling

Visualization

Error analysis

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years