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Related Experiment Video

Updated: Jul 6, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Semantic embedding based online cross-modal hashing method.

Meijia Zhang^1,2, Junzheng Li³, Xiyuan Zheng⁴

¹School of Data Science and Computer Science, Shandong Women's University, Jinan, 250300, China.

Scientific Reports

|January 6, 2024

Summary

This summary is machine-generated.

This study introduces Semantic Embedding-based Online Cross-modal Hashing (SEOCH) for efficient streaming data retrieval. SEOCH improves accuracy by leveraging semantic information and discrete optimization in online cross-modal hashing.

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Area of Science:

Computer Science
Information Retrieval
Machine Learning

Background:

Hashing is vital for efficient cross-modal retrieval in large-scale, high-dimensional data.
Existing offline hashing methods struggle with streaming data, while current online methods lack semantic integration and discrete optimization.

Purpose of the Study:

To develop an efficient and accurate online cross-modal hashing method for streaming data.
To enhance cross-modal retrieval by integrating semantic information and discrete optimization.

Main Methods:

Proposed Semantic Embedding-based Online Cross-modal Hashing (SEOCH) method.
Mapping semantic labels to a latent semantic space and constructing a semantic similarity matrix.
Employing a discrete optimization strategy for enhanced hashing efficiency.

Main Results:

SEOCH effectively leverages semantic information for improved cross-modal retrieval.
The discrete optimization strategy enhances the efficiency of online hashing.
Experimental results demonstrate the superiority of SEOCH over existing methods on multi-label datasets.

Conclusions:

SEOCH offers a superior approach to online cross-modal hashing for streaming data.
Integrating semantic embedding and discrete optimization addresses limitations of prior online methods.
The proposed method achieves both high accuracy and efficiency in cross-modal retrieval tasks.