Paper Info
Title
Zero-Shot Learning and Detection of Teeth in Images of Bat Skulls
Abstract
Biologists collect and analyze phenomic (e.g., anatomical or non-genomic) data to discover relationships among species in the Tree of Life. The domain is seeking to modernize this very time-consuming and largely manual process. We have developed an approach to detect and localize object parts in standardized images of bat skulls. This approach has been further developed for unannotated images by leveraging knowledge learned from a few annotated images. The key challenge is that the unlabeled images show bat skulls of "unknown" species that may have types, total numbers, and layouts of the teeth that differ from the "known" species appearing in the labeled images. Our method begins by matching the unlabeled images to the labeled ones. This allows a transfer of tooth annotations to the unlabeled images. We then learn a tree parts model on the transferred annotations, and apply this model to detect and label teeth in the unlabeled images. Our evaluation demonstrates good performance, which is close to our upper bound performance by the fully supervised model.
Year
DOI
Venue
2013
10.1109/ICCVW.2013.34
ICCVW '13 Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops
Keywords
Field
DocType
bat skulls,label tooth,upper bound performance,localize object part,annotated image,key challenge,good performance,supervised model,bat skull,unlabeled image,zero-shot learning,tree parts model,dentistry,learning artificial intelligence
Computer vision,Object detection,Pattern recognition,Image matching,Zero shot learning,Computer science,Upper and lower bounds,Artificial intelligence
Conference
Volume
Issue
Citations 
2013
1
0
PageRank 
References 
Authors
0.34
9
8
Name
Order
Citations
PageRank
Xu Hu1364.46
Michael S. Lam2302.89
Sinisa Todorovic3143180.44
Thomas G. Dietterich493361722.57
Maureen A. O'Leary500.34
Andrea L. Cirranello600.34
Nancy B. Simmons700.34
Paúl M. Velazco800.34