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Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval

Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval

Rong Yan, Apostol Natsev, Murray Campbell
Copyright: © 2009 |Pages: 26
ISBN13: 9781605661889|ISBN10: 1605661880|ISBN13 Softcover: 9781616926021|EISBN13: 9781605661896
DOI: 10.4018/978-1-60566-188-9.ch012
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MLA

Yan, Rong, et al. "Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval." Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, et al., IGI Global, 2009, pp. 272-297. https://doi.org/10.4018/978-1-60566-188-9.ch012

APA

Yan, R., Natsev, A., & Campbell, M. (2009). Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval. In D. Tao, D. Xu, & X. Li (Eds.), Semantic Mining Technologies for Multimedia Databases (pp. 272-297). IGI Global. https://doi.org/10.4018/978-1-60566-188-9.ch012

Chicago

Yan, Rong, Apostol Natsev, and Murray Campbell. "Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval." In Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, Dong Xu, and Xuelong Li, 272-297. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-188-9.ch012

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Abstract

Although important in practice, manual image annotation and retrieval has rarely been studied by means of formal modeling methods. In this chapter, the authors propose a set of formal models to characterize the annotation times for two commonly-used manual annotation approaches, that is, tagging and browsing. Based on the complementary properties of these models, the authors design new hybrid approaches, called frequency-based annotation and learning-based annotation, to improve the efficiency of manual image annotation as well as retrieval. Both our simulation and experimental results show that the proposed algorithms can achieve up to a 50% reduction in annotation time over baseline methods for manual image annotation, and produce significantly better annotation and retrieval results in the same amount of time.

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