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Advanced Similarity Search in Arbitrary Subspaces for Image Databases

Explore the innovative concept of searching for images in arbitrary subspaces based on feature vectors, enabling efficient similarity queries. This research focuses on improving query accuracy and speed by operating in tailored subspaces. Discover how this approach offers superior results compared to traditional full-feature space searches.

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Advanced Similarity Search in Arbitrary Subspaces for Image Databases

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  1. Similarity Search in Arbitrary Subspaces Xiang Lian, PhD Candidate, and Lei Chen, Assistant Professor {xlian, leichen}@cse.ust.hk Sponsored by MSRA Internet Services Theme Invitation Award • In image databases, each image is represented by a d-dimensionalfeature vector • A similarity query retrieves images whose feature vectors are within distance from a user-specified query vector • Instead of searching in the full feature space, our work aims to answer similarity queries in arbitrary subspaces images feature vectors query vector … … … subspace [1] Xiang Lian and Lei Chen. Similarity Search in Arbitrary Subspaces under Lp-Norm. In ICDE, 2008. [2] Xiang Lian and Lei Chen. A General Cost Model for Dimensionality Reduction in High Dimensional Spaces. In ICDE, 2007.

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