Abstract
Content-Based Image Retrieval systems provide a variety of usages. The most common one is target search, in which a user is trying to find a specific target image. Instead, we present, in this paper, a flexible image dataset browsing system. The user can browse the whole dataset looking for any ”interesting” image. To this aim, images are first abstracted through a set of signatures describing their color and texture composition. Afterwards, unsupervised clustering is performed to split the image set into several clusters of ”similar” images. Every cluster is represented by its centroid as an icon. The set of icons is presented to the user, who can pick one in order to see the images belonging to the cluster. Multi-dimensional scaling is used to visualize images in the same cluster by mapping the images onto a two-dimensional space. The experiments performed with a general-purpose image dataset consisting of one thousand images, categorized into ten classes, show the usefulness of the system.
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Julien, C., Saitta, L. (2008). Image Databases Browsing by Unsupervised Learning. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_42
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DOI: https://doi.org/10.1007/978-3-540-68123-6_42
Publisher Name: Springer, Berlin, Heidelberg
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