Welcome to the InfoSci Platform
Active Video Annotation: To Minimize Human Effort

Active Video Annotation: To Minimize Human Effort

Meng Wang, Xian-Sheng Hua, Jinhui Tang, Guo-Jun Qi
Copyright: © 2009 |Pages: 25
ISBN13: 9781605661889|ISBN10: 1605661880|ISBN13 Softcover: 9781616926021|EISBN13: 9781605661896
DOI: 10.4018/978-1-60566-188-9.ch013
Cite Chapter Cite Chapter

MLA

Wang, Meng, et al. "Active Video Annotation: To Minimize Human Effort." Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, et al., IGI Global, 2009, pp. 298-322. https://doi.org/10.4018/978-1-60566-188-9.ch013

APA

Wang, M., Hua, X., Tang, J., & Qi, G. (2009). Active Video Annotation: To Minimize Human Effort. In D. Tao, D. Xu, & X. Li (Eds.), Semantic Mining Technologies for Multimedia Databases (pp. 298-322). IGI Global. https://doi.org/10.4018/978-1-60566-188-9.ch013

Chicago

Wang, Meng, et al. "Active Video Annotation: To Minimize Human Effort." In Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, Dong Xu, and Xuelong Li, 298-322. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-188-9.ch013

Export Reference

Mendeley
Favorite

Abstract

This chapter introduces the application of active learning in video annotation. The insufficiency of training data is a major obstacle in learning-based video annotation. Active learning is a promising approach to dealing with this difficulty. It iteratively annotates a selected set of most informative samples, such that the obtained training set is more effective than that gathered randomly. The authors present a brief review of the typical active learning approaches. They categorize the sample selection strategies in these methods into five criteria, that is, risk reduction, uncertainty, positivity, density, and diversity. In particular, they introduce the Support Vector Machine (SVM)-based active learning scheme which has been widely applied. Afterwards, they analyze the deficiency of the existing active learning methods for video annotation, that is, in most of these methods the to-be-annotated concepts are treated equally without preference and only one modality is applied. To address these two issues, the authors introduce a multi-concept multi-modality active learning scheme. This scheme is able to better explore human labeling effort by considering both the learnabilities of different concepts and the potential of different modalities.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.