Algorithm

Universal hashing

Dynamic perfect hashing

Feature hashing

Double hashing

Pattern recognition (psychology)

Artificial intelligence

Block code

K-independent hashing

Hamming space

Subspace topology

Mathematics

Computer security

Computer science

Binary number

Decoding methods

Binary code

Hash table

Hamming code

Locality-sensitive hashing

Theoretical computer science

Arithmetic

Hash function

IEEE Transactions on Knowledge and Data Engineering

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.

Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing