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%.
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
SPIE 8297, Document Recognition and Retrieval XIX (2012: Jan. 22, Burlingame, CA)
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)
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Article - Conference proceedings
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