Graphical image classification combining an evolutionary algorithm and binary particle swarm optimization
Beibei Cheng, Renzhong Wang, Sameer Antani, R. Joe Stanley, George R. Thoma · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
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%.