Fundamentals of digital image processing
Fundamentals of digital image processing
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
NLP found helpful (at least for one text categorization task)
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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A picture is worth a thousand words. Biomedical researchers tend to incorporate a significant number of images (i.e., figures or tables) in their publications to report experimental results, to present research models, and to display examples of biomedical objects. Unfortunately, this wealth of information remains virtually inaccessible without automatic systems to organize these images. We explored supervised machine-learning systems using Support Vector Machines to automatically classify images into six representative categories based on text, image, and the fusion of both. Our experiments show a significant improvement in the average F-score of the fusion classifier (73.66%) as compared with classifiers just based on image (50.74%) or text features (68.54%).