TY - GEN
T1 - Novel image features for categorizing biomedical images
AU - Sheng, Jianqiang
AU - Xu, Songhua
AU - Deng, Weicai
AU - Luo, Xiaonan
PY - 2012
Y1 - 2012
N2 - Images embedded in biomedical publications are richly informative. For example, they often concisely summarize key hypotheses, illustrate new methods, and highlight major experimental findings in a research article. Prior studies [1] suggested that images embedded in biomedical publications offer effective clues for retrieving and mining their source documents. To facilitate accessing such valuable imagery resources, image categorization can be helpful. Like many other image processing tasks, extracting discriminative image features is critical for the success of image categorization. For biomedical images, we notice that many of them are embedded with abundant annotation text. Observing this property, we introduce a set of novel image features that exploit the spatial distribution of text information inside an image as essential clues for categorizing biomedical images. Through results of our evaluation experiments, this paper demonstrates the effectiveness of the proposed novel features - compared with conventional image features, our new features can help categorize biomedical images with superior performance using a standard supervised learning based approach.
AB - Images embedded in biomedical publications are richly informative. For example, they often concisely summarize key hypotheses, illustrate new methods, and highlight major experimental findings in a research article. Prior studies [1] suggested that images embedded in biomedical publications offer effective clues for retrieving and mining their source documents. To facilitate accessing such valuable imagery resources, image categorization can be helpful. Like many other image processing tasks, extracting discriminative image features is critical for the success of image categorization. For biomedical images, we notice that many of them are embedded with abundant annotation text. Observing this property, we introduce a set of novel image features that exploit the spatial distribution of text information inside an image as essential clues for categorizing biomedical images. Through results of our evaluation experiments, this paper demonstrates the effectiveness of the proposed novel features - compared with conventional image features, our new features can help categorize biomedical images with superior performance using a standard supervised learning based approach.
KW - image categorization
KW - novel image features
KW - spatial distribution of text information
UR - http://www.scopus.com/inward/record.url?scp=84872533603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872533603&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2012.6392689
DO - 10.1109/BIBM.2012.6392689
M3 - Conference contribution
AN - SCOPUS:84872533603
SN - 9781467325585
T3 - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
SP - 312
EP - 317
BT - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
T2 - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012
Y2 - 4 October 2012 through 7 October 2012
ER -