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%.
Recommended Citation
B. Cheng et al., "Graphical Image Classification Combining an Evolutionary Algorithm and Binary Particle Swarm Optimization," Proceedings of SPIE 8297, Document Recognition and Retrieval XIX (2012, Burlingame, CA), vol. 8297, SPIE -- The International Society for Optical Engineering, Jan 2012.
The definitive version is available at https://doi.org/10.1117/12.910533
Meeting Name
SPIE 8297, Document Recognition and Retrieval XIX (2012: Jan. 22, Burlingame, CA)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Biomedical journal; Classification accuracy; Clinical decision support; Feature-based; Flow charts; Graphical images; Image features; Learning approach; Multi-modal; Optimal subsets; Support vector; Feature extraction; Information retrieval; Support vector machines; Image processing; Binary Particle Swarm Optimization (BPSO); Evolutionary Algorithm (EA); Support Vector Machine (SVM)
International Standard Book Number (ISBN)
978-0-81948-944-9
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2012 SPIE -- The International Society for Optical Engineering, All rights reserved.
Publication Date
01 Jan 2012
