Automatic subspace clustering of high dimensional data for data mining applications
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Automatic image annotation and retrieval using weighted feature selection
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MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
OCRS: an interactive object-based image clustering and retrieval system
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Automatic Image Annotation Using Color K-Means Clustering
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
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International Journal of Computer Vision
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MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval.