In this paper, we propose a multi-label deep sparse hashing (MDSH) to learn compact binary codes for efficient image retrieval.
Abstract—In this paper, we propose a multi-label deep sparse hashing (MDSH) to learn compact binary codes for efficient image retrieval.
This paper performs deep network training such that optimal binary codes are obtained from a sparsity-based discriminative criterion and learns their ...
We need the efficient feature representation of heterogeneous and multi-modal data to learn high-quality discrete hash codes and improve retrieval performance.
Jan 7, 2022 · In this paper, we presented a superior cross-modal hashing method named multi-label enhancement based self-supervised deep cross-modal hashing (MESDCH).
Apr 16, 2018 · I am working on a model where i need to predict multiple attributes from an image: So, I am in a multi label classification situation.
Missing: Hashing. | Show results with:Hashing.
Supervised online hashing is mainly based on two kinds of supervision information: similarity matrix and label, which can narrow the semantic gap. Online Kernel.
A deep multi-similarity hashing technique based on multi-label semantics has been proposed in this paper as a means of a new controlled deep hashing system ...
Feb 2, 2021 · In this paper, we propose a rank-consistency deep hashing method (RCDH) to learn effective binary codes for scalable multi-label image retrieval ...
Experiments on four popular datasets demonstrate that the proposed method outperforms the competing methods and achieves the state-of-the-art performance in ...