人工智能
计算机科学
模式识别(心理学)
边距(机器学习)
相似性(几何)
图像检索
相似性学习
聚类分析
成对比较
公制(单位)
深度学习
嵌入
数据挖掘
机器学习
图像(数学)
经济
运营管理
作者
Congcong Duan,Yong Feng,Mingliang Zhou,Xiancai Xiong,Yongheng Wang,Baohua Qiang,Weijia Jia
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:19 (8): 9173-9182
标识
DOI:10.1109/tii.2022.3227721
摘要
Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep local descriptors for deep metric learning. We take the rich interclass similarity relationship based on the deep local invariant descriptors from the data into account to optimize sampling strategies for mining informative samples. The method dynamically adjusts the margin between data points to better match the true similarity relationship between classes. Specifically, for images in a batch, we first obtain deep local descriptors and calculate the similarity matrix of the channel, pixel, and spatial levels. Then, depending on the calculated comprehensive similarity matrix, we propose a multilevel similarity-aware loss function through the deviation between pairwise distance and violate margin to make full use of informative samples. The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods in terms of fine-grained image retrieval and clustering tasks.
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