Deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval.

ABSTRACT: Cross-modal hashing is an efficient method to embed high-dimensional heterogeneous modal feature descriptors into a consistency-preserving Hamming space with low-dimensional. Most existing cross-modal hashing methods have been able to bridge the heterogeneous modality gap, but there are still two challenges resulting in limited retrieval accuracy: (1) ignoring the continuous similarity of samples on manifold; (2) lack of discriminability of hash codes with the same semantics. To cope with these problems, we propose a Deep Consistency-Preserving Hash Auto-encoders model, called DCPHA, based on the multi-manifold property of the feature distribution. Specifically, DCPHA consists of a pair of asymmetric auto-encoders and two semantics-preserving attention branches working in the encoding and

SUBMITTER: Wang X 

PROVIDER: S-EPMC9911775 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature