Geodesy

Hash table

Pairwise comparison

Quantum mechanics

Semantic gap

Benchmark (surveying)

Hash function

Computer security

Semantic similarity

Rank (graph theory)

Eigenvalues and eigenvectors

Data mining

Computer science

Image retrieval

WordNet

Physics

Feature hashing

Pattern recognition (psychology)

Mathematics

Double hashing

Geography

Image (mathematics)

Artificial intelligence

Combinatorics

Information retrieval

Matrix decomposition

Machine learning

IEEE Transactions on Knowledge and Data Engineering

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised Cross-Modal Hashing (CMH) methods over unsupervised ones. Nevertheless, most existing supervised CMH methods still suffer from some limitations: 1) it is assumed that the observed labels of training data are complete and accurate, which may be impractical due to the missing and wrong class assignments in real applications, and 2) the semantic information is not fully excavated, especially for the semantic correlations among labels. To address these issues, this paper proposes a Weakly-supervised enhAnced Semantic-aware Hashing (WASH) method which simultaneously estimates the label noises and performs enhanced semantic-aware hash learning. WASH employs the low-rank and sparse decomposition to alleviate the label noises, and a high-level semantic factor as well as a semantic correlation matrix is obtained by low-rank factorization on the noise-reduced labels. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce the heterogeneity gaps, so as to enhance the semantic awareness of shared representation. In this way, the hash codes can be obtained by binarizing the shared representation with pairwise semantic similarity preserved. Experiments on several benchmark datasets verify the effectiveness of the proposed method in comparison with the state-of-the-art CMH approaches.

Weakly-Supervised Enhanced Semantic-Aware Hashing for Cross-Modal Retrieval