An on-line interactive self-adaptive image classification framework

Sannen, Davy; Nuttin, Marnix; Smith, Jim; Tahir, Muhammad Atif; Caleb-Solly, Praminda; Lughofer, Edwin; Eitzinger, Christian

Abstract

In this paper we present a novel image classification framework, which is able to automatically re-configure and adapt its feature-driven classifiers and improve its performance based on user interaction during on-line processing mode. Special emphasis is placed on the generic applicability of the framework to arbitrary surface inspection systems. The basic components of the framework include: recognition of regions of interest (objects), adaptive feature extraction, dealing with hierarchical information in classification, initial batch training with redundancy deletion and feature selection components, on-line adaptation and refinement of the classifiers based on operators' feedback, and resolving contradictory inputs from several operators by ensembling outputs from different individual classifiers. The paper presents an outline on each of these components and concludes with a thorough discussion of basic and improved off-line and on-line classification results for artificial data sets and real-world images recorded during a CD imprint production process. © 2008 Springer-Verlag Berlin Heidelberg.

• Presentation Conference Type: Conference Paper (published)
• Publication Date: Jun 9, 2008
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Not Peer Reviewed
• Volume: 5008 LNCS
• Pages: 171-180
• DOI: https://doi.org/10.1007/978-3-540-79547-6_17
• Keywords: image classification, feature extraction, on-line adaptation and evolution, resolving contradictory inputs, classifier ensembles
• Public URL: https://uwe-repository.worktribe.com/output/1021638
• Publisher URL: http://dx.doi.org/10.1007/978-3-540-79547-6_17