We propose a novel unsupervised deep hashing framework to learn compact binary codes, which takes the quadruplet forms as input units.

Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval. Q. Qin, L. Huang, Z. Wei, J. Nie, K. Xie, and J. Hou. Inf. Sci., (2021 ).

Aug 1, 2021 · Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval ... Lecture Notes in Computer Science2013: 283-290被引量：3.

Apr 18, 2024 · In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), ...

2021: Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval Information Sciences 567: 116-130 · Dong, X.; Liu, L.; Zhu, L ...

Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval. Inf. Sci. 567: 116-130 (2021). [j6]. view. electronic edition via DOI ...

A novel Deep Adaptive Quadruplet Hashing with probability sampling (DAQH) for discriminative binary code learning with extensive experimental results on ...

This paper converts the unsupervised DH model into supervised by discovering pseudo labels, and proposes a novel unsuper supervised framework that unifies ...

... Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval ... Unsupervised deep hashing with node representation for image retrieval.

In order to achieve efficient similarity searching, hash functions are designed to encode images into lowdimensional binary codes with the constraint that ...