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Machine-Assisted Annotation of Forensic Imagery

Abstract: 

Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections. However, leading approaches rely on large samples of completely and accurately annotated images. In the case of a large forensic collection that we are aiming to annotate, neither the complete annotation nor the large training samples can be feasibly produced. We, therefore, investigate ways to assist manual annotation efforts done by forensic experts. We present a method that can propose both images and areas within an image likely to contain desired classes. Evaluation of the method with human annotators showed highly accurate classification and reasonable segmentation accuracy that was strongly affected by transfer learning.
We hope this effort can be helpful in other domains that require weak segmentation and have limited availability of qualified annotators.

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The rest of the authors are: Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus

Paper Details

Authors:
Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus
Submitted On:
16 September 2019 - 10:28am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Audris Mockus
Paper Code:
3417
Document Year:
2019
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SaraMousaviICIP19Poster.pdf

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[1] Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus, "Machine-Assisted Annotation of Forensic Imagery", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4641. Accessed: Sep. 18, 2020.
@article{4641-19,
url = {http://sigport.org/4641},
author = {Sara Mousavi; Ramin Nabati; Megan Kleeschulte; Dawnie Steadman; Audris Mockus },
publisher = {IEEE SigPort},
title = {Machine-Assisted Annotation of Forensic Imagery},
year = {2019} }
TY - EJOUR
T1 - Machine-Assisted Annotation of Forensic Imagery
AU - Sara Mousavi; Ramin Nabati; Megan Kleeschulte; Dawnie Steadman; Audris Mockus
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4641
ER -
Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus. (2019). Machine-Assisted Annotation of Forensic Imagery. IEEE SigPort. http://sigport.org/4641
Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus, 2019. Machine-Assisted Annotation of Forensic Imagery. Available at: http://sigport.org/4641.
Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus. (2019). "Machine-Assisted Annotation of Forensic Imagery." Web.
1. Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Dawnie Steadman, Audris Mockus. Machine-Assisted Annotation of Forensic Imagery [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4641