Abstract
The similarity search is a central problem to many applications, such as multimedia databases and repositories containing complex non-structured objects. The metric space model is very useful in these scenarios, because metric indexes support efficient similarity search but most of them are designed for main memory. In this article we introduce an improved version of the List of Clustered Permutations (iLCP), a competitive index for approximate similarity search. Our proposal is specially adapted for secondary memory and performs well in several scenarios, especially on spaces of medium and high dimensionality. We assessed this new structure with several real-life metric spaces from SISAP, the results show that this new version keeps the rewarding characteristics of LCP, while obtaining a very good performance in terms of number of pages read per search.
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Figueroa, K., Reyes, N., Camarena-Ibarrola, A., Valero-Elizondo, L. (2018). Improving the List of Clustered Permutation on Metric Spaces for Similarity Searching on Secondary Memory. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_9
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