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Related Experiment Video

Updated: Feb 8, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Triplet-Based Deep Hashing Network for Cross-Modal Retrieval.

Cheng Deng, Zhaojia Chen, Xianglong Liu

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

|July 12, 2018

Summary

This study introduces a triplet-based deep hashing (TDH) network for improved cross-modal retrieval. The TDH network enhances hash code quality by capturing richer semantic relationships, leading to superior search performance.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Hashing techniques offer efficient data storage and retrieval.
Cross-modal hashing is crucial for multimedia similarity search.
Existing methods often overlook semantic similarities between heterogeneous data, limiting retrieval accuracy.

Purpose of the Study:

To propose a novel triplet-based deep hashing (TDH) network for cross-modal retrieval.
To enhance the quality and discriminative power of hash codes.
To improve the performance of cross-modal similarity search.

Main Methods:

Utilizing triplet labels to capture generalized semantic correlations between cross-modal instances.
Developing a loss function considering both inter-modal and intra-modal views.
Incorporating graph regularization to maintain semantic similarity in Hamming space.

Main Results:

The proposed TDH network effectively captures richer semantic information.
The method demonstrates superior discriminative abilities for hash codes.
Experimental results show state-of-the-art performance on benchmark datasets.

Conclusions:

The triplet-based deep hashing network significantly advances cross-modal retrieval.
TDH offers a robust approach for leveraging semantic relationships in heterogeneous data.
The method provides a powerful solution for multimedia similarity search applications.