Semi-supervised Multimodal Hashing

TL;DR

This paper introduces a semi-supervised multimodal hashing method that effectively uses partial labels to generate binary codes for cross-modal data retrieval, reducing labeling effort while maintaining high performance.

Contribution

It proposes a novel semi-supervised approach that leverages fuzzy logic and label estimation to improve multimodal hashing with limited labeled data.

Findings

Achieves near-supervised performance with 50% labels

Outperforms some supervised methods with only 10% labels

Reduces need for extensive manual labeling in multimodal retrieval

Abstract

Retrieving nearest neighbors across correlated data in multiple modalities, such as image-text pairs on Facebook and video-tag pairs on YouTube, has become a challenging task due to the huge amount of data. Multimodal hashing methods that embed data into binary codes can boost the retrieving speed and reduce storage requirement. As unsupervised multimodal hashing methods are usually inferior to supervised ones, while the supervised ones requires too much manually labeled data, the proposed method in this paper utilizes a part of labels to design a semi-supervised multimodal hashing method. It first computes the transformation matrices for data matrices and label matrix. Then, with these transformation matrices, fuzzy logic is introduced to estimate a label matrix for unlabeled data. Finally, it uses the estimated label matrix to learn hashing functions for data in each modality to generate a unified binary code matrix. Experiments show that the proposed semi-supervised method with 50% labels can get a medium performance among the compared supervised ones and achieve an approximate performance to the best supervised method with 90% labels. With only 10% labels, the proposed method can still compete with the worst compared supervised one.