Probabilistic models for topic learning from images and captions in online biomedical literatures

Biomedical images and captions are one of the major sources of information in online biomedical publications. They often contain the most important results to be reported, and provide rich information about the main themes in published papers. In the data mining and information retrieval community, there has been much effort on using text mining and language modeling algorithms to extract knowledge from the text content of online biomedical publications; however, the problem of knowledge extraction from biomedical images and captions has not been fully studied yet. In this paper, a hierarchical probabilistic topic model with background distribution (HPB) is introduced to uncover the latent semantic topics from the co-occurrence patterns of caption words, visual words and biomedical concepts. With downloaded biomedical figures, restricted captions are extracted with regard to each individual image panel. During the indexing stage, the ‘bag-of-words’ representation of captions is su...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Xin Chen, Caimei Lu, Yuan An, Palakorn Achananuparp
• Keywords: Biomedical, Biomedical Image, CIKM 2009, Online Biomedical Publications