Improving Image Retrieval by Clustering

Improving Image Retrieval by Clustering

Dany Gebara (University of Calgary, Canada) and Reda Alhajj (University of Calgary, Canada)
DOI: 10.4018/978-1-60566-174-2.ch002


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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