Welcome to the InfoSci Platform
Image/Video Semantic Analysis by Semi-Supervised Learning

Image/Video Semantic Analysis by Semi-Supervised Learning

Jinhui Tang, Xian-Sheng Hua, Meng Wang
Copyright: © 2009 |Pages: 28
ISBN13: 9781605661889|ISBN10: 1605661880|ISBN13 Softcover: 9781616926021|EISBN13: 9781605661896
DOI: 10.4018/978-1-60566-188-9.ch008
Cite Chapter Cite Chapter

MLA

Tang, Jinhui, et al. "Image/Video Semantic Analysis by Semi-Supervised Learning." Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, et al., IGI Global, 2009, pp. 183-210. https://doi.org/10.4018/978-1-60566-188-9.ch008

APA

Tang, J., Hua, X., & Wang, M. (2009). Image/Video Semantic Analysis by Semi-Supervised Learning. In D. Tao, D. Xu, & X. Li (Eds.), Semantic Mining Technologies for Multimedia Databases (pp. 183-210). IGI Global. https://doi.org/10.4018/978-1-60566-188-9.ch008

Chicago

Tang, Jinhui, Xian-Sheng Hua, and Meng Wang. "Image/Video Semantic Analysis by Semi-Supervised Learning." In Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, Dong Xu, and Xuelong Li, 183-210. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-188-9.ch008

Export Reference

Mendeley
Favorite

Abstract

The insufficiency of labeled training samples is a major obstacle in automatic semantic analysis of large scale image/video database. Semi-supervised learning, which attempts to learn from both labeled and unlabeled data, is a promising approach to tackle this problem. As a major family of semi-supervised learning, graph-based methods have attracted more and more recent research. In this chapter, a brief introduction is given on popular semi-supervised learning methods, especially the graph-based methods, as well as their applications in the area of image annotation, video annotation, and image retrieval. It is well known that the pair-wise similarity is an essential factor in graph propagation based semisupervised learning methods. A novel graph-based semi-supervised learning method, named Structure- Sensitive Anisotropic Manifold Ranking (SSAniMR), is derived from a PDE based anisotropic diffusion framework. Instead of using Euclidean distance only, SSAniMR further takes local structural difference into account to more accurately measure pair-wise similarity. Finally some future directions of using semi-supervised learning to analyze the multimedia content are discussed.

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.