NDLI: May the torcher light our way: A negative-accelerated active learning framework for image classification

Content Provider	IEEE Xplore Digital Library
Author	Zhipeng Ye Peng Liu Xianglong Tang Wei Zhao
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China (Zhipeng Ye; Peng Liu; Xianglong Tang; Wei Zhao)
Abstract	Uncertainty sampling is one of the most widely used strategy for pool-based active learning, however, there exists the problem that selected images do not reflect the desired training distribution and need additional labeling cost. To deal with this problem, from aspects of image classification and visual perception, we improve the traditional entropy-based sampling strategy by introducing bag-of-visual-words classification method and negative-accelerated learning principle from Rescorla-Wagner perceptive model. Differs from previous researches that treated sampling and classifying process separately, under the unified negative-accelerated learning model, we combine the two processes as a uniform model, named as negative-accelerated uncertainty sampling strategy with BoVW (NUSB) by proposing a new evolving sample selection measure, which takes category distribution into consideration. Classifier is trained to provide category distribution for the sampling process, reducing additional cost of annotation. Also, transfer test is utilized to prevent over-fitting and further evaluate the performance of different sampling strategies. Experimental results on real world datasets show that our active sampling framework outperforms both baseline active sampling strategies and state-of-the-art active learning based image classification method.
Starting Page	1658
Ending Page	1662
File Size	847223
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Training Testing Semantics Google Labeling Measurement uncertainty negative-accelerated principle Image classification active learning bag-of-visual-words entropy-based sampling
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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