Generic and optimized framework for multi-content analysis based on learning approaches

Quentin Besnehard; Cédric Marchessoux; Tom Kimpe

doi:10.1117/12.838616

10 February 2010 Generic and optimized framework for multi-content analysis based on learning approaches

Quentin Besnehard, Cédric Marchessoux, Tom Kimpe

Proceedings Volume 7540, Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV; 75400V (2010) https://doi.org/10.1117/12.838616
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

During the European Cantata project (ITEA project, 2006-2009), a Multi-Content Analysis framework for the classification of compound images in various categories (text, graphical user interface, medical images, other complex images) was developed within Barco. The framework consists of six parts: a dataset, a feature selection method, a machine learning based Multi-Content Analysis (MCA) algorithm, a Ground Truth, an evaluation module based on metrics and a presentation module. This methodology was built on a cascade of decision tree-based classifiers combined and trained with the AdaBoost meta-algorithm. In order to be able to train these classifiers on large training datasets without excessively increasing the training time, various optimizations were implemented. These optimizations were performed at two levels: the methodology itself (feature selection / elimination, dataset pre-computation) and the decision-tree training algorithm (binary threshold search, dataset presorting and alternate splitting algorithm). These optimizations have little or no negative impact on the classification performance of the resulting classifiers. As a result, the training time of the classifiers was significantly reduced, mainly because the optimized decision-tree training algorithm has a lower algorithmic complexity. The time saved through this optimized methodology was used to compare the results of a greater number of different training parameters.

Citation Download Citation

Quentin Besnehard, Cédric Marchessoux, and Tom Kimpe "Generic and optimized framework for multi-content analysis based on learning approaches", Proc. SPIE 7540, Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV, 75400V (10 February 2010); https://doi.org/10.1117/12.838616

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