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Law of Independent Assortment02:03

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Related Experiment Video

Updated: May 27, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Kernelized locality-sensitive hashing.

Brian Kulis1, Kristen Grauman

  • 1Computer Science and Engineering Department, Ohio State University, 395 Dreese Labs, Columbus, OH 43210, USA. kulis@cse.ohio-state.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized locality-sensitive hashing method for efficient similarity search in high-dimensional kernelized data, even with unknown embeddings. This advances fast retrieval for vision applications like image retrieval and object classification.

Related Experiment Videos

Last Updated: May 27, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Computer Vision
  • Machine Learning
  • Data Mining

Background:

  • Fast retrieval is crucial for large-scale vision applications.
  • Existing methods struggle with high-dimensional kernelized data and unknown embeddings.
  • Efficient search in Hamming space is desirable.

Purpose of the Study:

  • To generalize locality-sensitive hashing (LSH) for arbitrary kernel functions.
  • To enable sublinear time similarity search for kernelized data with unknown embeddings.
  • To improve performance in image retrieval and other vision tasks.

Main Methods:

  • Developed a generalized locality-sensitive hashing technique.
  • Accommodated arbitrary kernel functions within the LSH framework.
  • Preserved sublinear time similarity search guarantees.

Main Results:

  • The generalized LSH method effectively handles high-dimensional kernelized data.
  • Achieved accurate and fast performance on various vision datasets.
  • Demonstrated utility in object classification, feature matching, and content-based retrieval.

Conclusions:

  • The proposed method extends LSH to a wider class of similarity functions.
  • Enables efficient similarity search for kernelized data with unknown embeddings.
  • Offers significant advantages for image retrieval and related computer vision problems.