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Proceedings Paper

Graphical image classification combining an evolutionary algorithm and binary particle swarm optimization
Author(s): Beibei Cheng; Renzhong Wang; Sameer Antani; R. Joe Stanley; George R. Thoma
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Paper Abstract

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted image features that are then classified using a Support Vector Machine (SVM) classifier. Evaluation performed on 1038 figure images extracted from ten BioMedCentral® journals with the features selected by EABPSO yielded classification accuracy as high as 87.5%.

Paper Details

Date Published: 23 January 2012
PDF: 8 pages
Proc. SPIE 8297, Document Recognition and Retrieval XIX, 829703 (23 January 2012); doi: 10.1117/12.910533
Show Author Affiliations
Beibei Cheng, Missouri Univ. of Science and Technology (United States)
Renzhong Wang, Missouri Univ. of Science and Technology (United States)
Sameer Antani, National Library of Medicine (United States)
R. Joe Stanley, Missouri Univ. of Science and Technology (United States)
George R. Thoma, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 8297:
Document Recognition and Retrieval XIX
Christian Viard-Gaudin; Richard Zanibbi, Editor(s)

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