Effective and Efficient Indexing in Cross-Modal Hashing-Based Datasets

TL;DR

This paper introduces a novel probability-based indexing method for cross-modal hashing that improves retrieval accuracy and efficiency by using a neural network to optimize binary hash code indexing.

Contribution

It proposes a new index scheme utilizing binary bits and neural network training to enhance cross-modal retrieval performance and speed.

Findings

Significant improvement in retrieval accuracy on benchmark datasets.

Enhanced efficiency in search process through index optimization.

Compatibility with multiple existing hashing methods.

Abstract

To overcome the barrier of storage and computation, the hashing technique has been widely used for nearest neighbor search in multimedia retrieval applications recently. Particularly, cross-modal retrieval that searches across different modalities becomes an active but challenging problem. Although dozens of cross-modal hashing algorithms are proposed to yield compact binary codes, the exhaustive search is impractical for the real-time purpose, and Hamming distance computation suffers inaccurate results. In this paper, we propose a novel search method that utilizes a probability-based index scheme over binary hash codes in cross-modal retrieval. The proposed hash code indexing scheme exploits a few binary bits of the hash code as the index code. We construct an inverted index table based on index codes and train a neural network to improve the indexing accuracy and efficiency. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated by three cross-modal hashing methods. Results show the proposed method effectively boost the performance on these hash methods.

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