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Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond
Published on: July 27, 2013
A stacked graphical model for associating sub-images with sub-captions
Zhenzhen Kou1, William W Cohen, Robert F Murphy
1Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu
Abstract:
There is extensive interest in mining data from full text. We have built a system called SLIF (for Subcellular Location Image Finder), which extracts information on one particular aspect of biology from a combination of text and images in journal articles. Associating the information from the text and image requires matching sub-figures with the sentences in the text. We introduce a stacked graphical model, a meta-learning scheme to augment a base learner by expanding features based on related instances, to match the labels of sub-figures with labels of sentences. The experimental results show a significant improvement in the matching accuracy of the stacked graphical model (81.3%) as compared with a relational dependency network (70.8%) or the current algorithm in SLIF (64.3%).
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