NDLI: Ensembles of Support Vector Data Description for Active Learning Based Annotation of Affective Corpora

Content Provider	IEEE Xplore Digital Library
Author	Thiam, P. Kachele, M. Schwenker, F. Palm, G.
Copyright Year	2015
Description	Author affiliation: Inst. of Neural Inf. Process., Ulm Univ., Ulm, Germany (Thiam, P.; Kachele, M.; Schwenker, F.; Palm, G.)
Abstract	The present work aims primary at developing an approach to detect irregular and spontaneous facial gestures in video sequences. The developed approach should help a system distinguish between neutral facial expressions characterized by the absence of facial gestures and facial events characterized by the presence of observable facial gestures in video sequences. For this purpose, an active learning approach is proposed in order to avoid the task of annotating an entire video sequence before proceeding with the classification. It is well known that the annotation task is hard, expensive and error prone. Each video sequence is segmented into smaller segments that are then to be investigated and annotated based on the absence or presence of facial gestures. The approach consists in first selecting a set of samples classified as uncharacteristic through the majority vote of a committee of support vector data description (SVDD) models generated randomly. The base learner then focuses on the selected outliers and not on the whole annotated corpus. Different query strategies are used to select the most informative samples among the selected outliers. Those samples are then annotated by the user and added to a pool of annotated samples. The latter is subsequently used to train the base learner again before the next iteration can take place. Experiments suggest that the proposed active learning approach performs as well as a system trained on a fully annotated corpus, while dramatically reducing the cost of annotation.
Starting Page	1801
Ending Page	1807
File Size	407849
Page Count	7
File Format	PDF
ISBN	9781479975600
DOI	10.1109/SSCI.2015.251
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-07
Publisher Place	South Africa
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Video sequences Information processing Predictive models Electronic mail Mathematical model Optimization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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