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Improving Image Retrieval by Clustering

Improving Image Retrieval by Clustering

Dany Gebara, Reda Alhajj
Copyright: © 2009 |Pages: 24
ISBN13: 9781605661742|ISBN10: 1605661740|ISBN13 Softcover: 9781616925635|EISBN13: 9781605661759
DOI: 10.4018/978-1-60566-174-2.ch002
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MLA

Gebara, Dany, and Reda Alhajj. "Improving Image Retrieval by Clustering." Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, IGI Global, 2009, pp. 20-43. https://doi.org/10.4018/978-1-60566-174-2.ch002

APA

Gebara, D. & Alhajj, R. (2009). Improving Image Retrieval by Clustering. In Z. Ma (Ed.), Artificial Intelligence for Maximizing Content Based Image Retrieval (pp. 20-43). IGI Global. https://doi.org/10.4018/978-1-60566-174-2.ch002

Chicago

Gebara, Dany, and Reda Alhajj. "Improving Image Retrieval by Clustering." In Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, 20-43. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-174-2.ch002

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Abstract

This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process starts by extracting a feature vector for each image; wavelets are employed in the process. Then the images (each represented by its feature vector) are classified into groups by employing a density-based clustering approach, namely OPTICS. This highly improves the querying facility by limiting the search space to a single cluster instead of the whole database. The cluster to be searched is determined by applying on the query image the same clustering process OPTICS. This leads to the closest cluster to the query image, and hence, limits the search to the latter cluster without adding the query image to the cluster, except if such request is explicitly specified. The power of this system is demonstrated on non-texture images from the Corel dataset. The achieved results demonstrate that the classification of images is extremely fast and accurate.

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