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Related Experiment Video

Updated: May 20, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Spline regression hashing for fast image search.

Yang Liu¹, Fei Wu, Yi Yang

¹College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. liuy@cs.zju.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 18, 2012

Summary

This study introduces spline regression hashing for faster image retrieval. The novel method precisely preserves data similarity and generates hash codes for unseen images, outperforming existing techniques.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Rapid growth of web images necessitates efficient retrieval techniques.
Hashing technologies represent images as binary codes for fast similarity measurement using Hamming distance.

Purpose of the Study:

To improve precise similarity structure preservation in image hashing.
To address the challenge of generating hash codes for previously unseen data (out-of-sample problem).

Main Methods:

Proposed a spline regression hashing method exploiting local and global data similarity structures.
Introduced splines in Sobolev space to capture local manifold structure via a data mapping function.
Developed a framework that simultaneously learns training data hash codes and a hash function for unseen data.

Main Results:

The proposed method effectively preserves both local and global data similarity.
Successfully solved the out-of-sample problem by learning a generalized hash function.
Demonstrated superior performance compared to state-of-the-art techniques in extensive experiments.

Conclusions:

Spline regression hashing offers a robust solution for accurate and efficient large-scale image retrieval.
The method's ability to handle unseen data makes it practical for real-world applications.
The approach advances the field of hashing for large-scale image databases.