Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Digital Image Processing
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Accessing bioscience images from abstract sentences
Bioinformatics
Integrating image data into biomedical text categorization
Bioinformatics
Bioinformatics
Introduction to Information Retrieval
Introduction to Information Retrieval
Bioinformatics
Structured correspondence topic models for mining captioned figures in biological literature
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring text and image features to classify images in bioscience literature
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Millions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and integrated them with text features. We performed feature selection and explored different classification models, including a rule-based figure classifier, a supervised machine-learning classifier, and a multi-model classifier, the latter of which integrated the first two classifiers. Our results show that feature selection improved figure classification and the novel image features we explored were the best among image features that we have examined. Our results also show that integrating text and image features achieved better performance than using either of them individually. The best system is a multi-model classifier which combines the rule-based hierarchical classifier and a support vector machine (SVM) based classifier, achieving a 76.7% F1-score for five-type classification. We demonstrated our system at http://figureclassification.askhermes.org/.