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The Imapct of Low-Level Features in Semantic-Based Image

The Imapct of Low-Level Features in Semantic-Based Image

Konstantinos Konstantinidis, Antonios Gasteratos, Ioannis Andreadis
Copyright: © 2007 |Pages: 23
ISBN13: 9781599043708|ISBN10: 159904370X|ISBN13 Softcover: 9781599043715|EISBN13: 9781599043722
DOI: 10.4018/978-1-59904-370-8.ch002
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MLA

Konstantinidis, Konstantinos, et al. "The Imapct of Low-Level Features in Semantic-Based Image." Semantic-Based Visual Information Retrieval, edited by Yu-Jin Zhang, IGI Global, 2007, pp. 23-45. https://doi.org/10.4018/978-1-59904-370-8.ch002

APA

Konstantinidis, K., Gasteratos, A., & Andreadis, I. (2007). The Imapct of Low-Level Features in Semantic-Based Image. In Y. Zhang (Ed.), Semantic-Based Visual Information Retrieval (pp. 23-45). IGI Global. https://doi.org/10.4018/978-1-59904-370-8.ch002

Chicago

Konstantinidis, Konstantinos, Antonios Gasteratos, and Ioannis Andreadis. "The Imapct of Low-Level Features in Semantic-Based Image." In Semantic-Based Visual Information Retrieval, edited by Yu-Jin Zhang, 23-45. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-370-8.ch002

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Abstract

Image Retrieval (IR) is generally known as a collection of techniques for retrieving images on the basis of features, either low-level (Content-based IR) or high-level (Semantic-based IR). Since Semantic-based features rely on low-level ones, in this chapter the reader is initially familiarized with the most widely used low-level features. An efficient way to present these features is by means of a statistical tool capable of bearing concrete information, such as the histogram. For use in IR, the histograms extracted from the previously mentioned features need to be compared by means of a metric. The most popular methods and distances are, thus, apposed. Finally, a number of IR systems using histograms are presented in a thorough manner and their experimental results are discussed. The steps in order to develop a custom IR system, along with modern techniques in image feature extraction are also presented.

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