23 January 2012 Graphical image classification combining an evolutionary algorithm and binary particle swarm optimization
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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%.
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Beibei Cheng, Beibei Cheng, Renzhong Wang, Renzhong Wang, Sameer Antani, Sameer Antani, R. Joe Stanley, R. Joe Stanley, George R. Thoma, George R. Thoma, "Graphical image classification combining an evolutionary algorithm and binary particle swarm optimization", Proc. SPIE 8297, Document Recognition and Retrieval XIX, 829703 (23 January 2012); doi: 10.1117/12.910533; https://doi.org/10.1117/12.910533
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