Unsupervised learning of arbitrarily shaped clusters with application to image database categorization

Hichem Frigui

doi:10.1117/12.476293

10 January 2003 Unsupervised learning of arbitrarily shaped clusters with application to image database categorization

Hichem Frigui

Proceedings Volume 5021, Storage and Retrieval for Media Databases 2003; (2003) https://doi.org/10.1117/12.476293
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

Clustering is considered as one of the most important tools to organize and analyze large multimedia databases. In Content Based Image Retrieval (CBIR), Clustering can be used to categorize a large collection of images. This organization can be used to: (i) build an indexing structure; (ii) build a navigation system; or (iii) show the user the most representative images in a query by visual example. Most existing clustering techniques assume that the clusters have well-defined shapes (spherical or ellipsoidal). Thus, they are not suitable for image database categorization where images are usually mapped to high-dimensional feature vectors, and it is hard to even guess the shape of the clusters in the feature space. In this paper, we first describe a clustering approach, called SyMP, that can identify clusters of various shapes. Then, we demonstrate its ability to generate an efficient and compact summary of an image database. SyMP is based on synchronization of pulse-coupled oscillators. It is robust to noise and outliers, determines the number of clusters in an unsupervised manner, and identifies clusters of arbitrary shapes. The robustness of SyMP is an intrinsic property of the synchronization mechanism. To determine the optimum number of clusters, SyMP uses a dynamic and cluster dependent resolution parameter. To identify clusters of various shapes, SyMP models each cluster by an ensemble of Gaussian components.

Citation Download Citation

Hichem Frigui "Unsupervised learning of arbitrarily shaped clusters with application to image database categorization", Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); https://doi.org/10.1117/12.476293

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KEYWORDS

Oscillators

Databases

Prototyping

Feature extraction

Distance measurement

Image retrieval

Machine learning

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