散列函数
计算机科学
图像检索
相似性(几何)
模式识别(心理学)
二进制数
人工智能
局部敏感散列
二进制代码
图像(数学)
哈希表
数学
算术
计算机安全
作者
Yuchen Liang,Yan Pan,Hanjiang Lai,Wei Liu,Jian Yin
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:31: 949-961
被引量:6
标识
DOI:10.1109/tip.2021.3137653
摘要
Hashing is a practical approach for the approximate nearest neighbor search. Deep hashing methods, which train deep networks to generate compact and similarity-preserving binary codes for entities (e.g. images), have received lots of attention in the information retrieval community. A representative stream of deep hashing methods is triplet-based hashing that learns hashing models from triplets of data. The existing triplet-based hashing methods only consider triplets that are in the form of (q,q+,q-) , where q and q+ are in the same class and q and q- are in different classes. However, the number of possible triplets is approximately the cube of training examples, triplets used in the existing methods are only a small fraction of all possible triplets. This motivates us to develop a new triplet-based hashing method that adopts many more triplets in training phase. We propose Deep Listwise Triplet Hashing (DLTH) that introduces more triplets into batch-based training and a novel listwise triplet loss to capture the relative similarity in new triplets. This method has a pipeline of two steps. In Step 1, we propose a novel way to generate triplets from the soft class labels obtained by knowledge distillation module, where the triplets in the form of (q,q+,q-) are a subset of the newly obtained triplets. In Step 2, we develop a novel listwise triplet loss to train the hashing network, which seeks to capture the relative similarity between images in triplets according to soft labels. We conduct comprehensive image retrieval experiments on four benchmark datasets. The experimental results show that the proposed method has superior performances over state-of-the-art baselines.
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