By Topic


Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Juchang Hua ; Center for Bioimage Informatics, Carnegie Mellon Univ., Pittsburgh, PA ; Orhan N. Ayasli ; William W. Cohen ; Robert F. Murphy

We have previously built a subcellular location image finder (SLIP) system, which extracts information regarding protein subcellular location patterns from both text and images in journal articles. One important task in SLIP is to identify fluorescence microscope images. To improve the performance of this binary classification problem, a set of 7 edge features extracted from images and a set of "bag of words" text features extracted from text have been introduced in addition to the 64 intensity histogram features we have used previously. An overall accuracy of 88.6% has been achieved with an SVM classifier. A co-training algorithm has also been applied to the problem to utilize the unlabeled dataset and it substantially increases the accuracy when the training set is very small but can contribute very little when the training set is large.

Published in:

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

12-15 April 2007