Abstract
This paper proposes a novel clustering based indexing approach called GMM-ClusterForest for supporting multi-features based similarity search in high-dimensional spaces. We fit a Gaussian Mixture Model (GMM) to data through the Expectation-Maximization (EM) algorithm for estimating GMM parameters and the Minimum Description Length (MDL) criterion for selecting GMM structure. Each Gaussian component in the GMM is taken as a cluster center and each data point is assigned to the cluster according to the Bayesian decision rule. By performing this clustering method hierarchically, an index tree is constructed and the corresponding similarity search method is developed for a type of features. Then multi-features based similarity search is fulfilled by fusing the index trees for all the types of features considered. We evaluated the proposed indexing approach through applying it to example-based image retrieval and conducting the experiments on Corel 1000 dataset and self-collected large dataset. The experimental results show that our approach is effective and promising.
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Wan, Y. et al. (2012). GMM-ClusterForest: A Novel Indexing Approach for Multi-features Based Similarity Search in High-Dimensional Spaces. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_26
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DOI: https://doi.org/10.1007/978-3-642-34481-7_26
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