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Average Approximate Hashing-Based Double Projections Learning for Cross-Modal Retrieval.

Xiaozhao Fang, Kaihang Jiang, Na Han

IEEE Transactions on Cybernetics

|June 9, 2021

Summary

This summary is machine-generated.

Average Approximate Hashing (AAH) enhances cross-modal retrieval by preserving data locality and reconstruction residuals. This novel method projects data into approximate semantic spaces, outperforming existing techniques in multimedia database searches.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval is vital for efficient multimedia database searching.
Matrix factorization methods for cross-modal hashing have limitations, including ignoring reconstruction residuals and data energy loss.
Projecting diverse data modalities into a single semantic space is suboptimal due to differing data properties.

Purpose of the Study:

To propose a novel Average Approximate Hashing (AAH) method for improved cross-modal retrieval.
To address limitations of existing matrix factorization techniques in preserving data characteristics.
To develop a hashing method that handles multi-modal data properties effectively.

Main Methods:

Integrated locality and residual preservation into a graph embedding framework using label information.
Projected data from different modalities into separate, approximating semantic spaces.
Introduced a Principal Component Analysis (PCA)-like projection matrix to preserve data energy.

Main Results:

The proposed AAH method demonstrated superior performance compared to state-of-the-art cross-modal hashing techniques.
AAH effectively preserves data locality, reconstruction residuals, and main energy during hashing.
Experimental results on standard databases validate the efficacy of the AAH approach.

Conclusions:

Average Approximate Hashing (AAH) offers a significant advancement in cross-modal retrieval.
The method's ability to handle multi-modal data properties and preserve key data characteristics leads to improved search efficiency.
AAH provides a robust framework for learning hash codes in large-scale multimedia databases.