显著区域抑制与多尺度特征融合的建筑风格识别
Salient region suppression and multi-scale feature fusion for archi-tectural style recognition
孟月波 1刘佳 1赵敏华 1刘光辉1
作者信息
- 1. 西安建筑科技大学信息与控制工程学院,陕西西安 710055
- 折叠
摘要
针对建筑元素特征提取不全、相似建筑风格识别困难等问题,提出一种显著区域抑制与多尺度特征融合(salient region suppression and multi-scale feature fusion,SRSMSFF)的建筑风格识别方法.首先,采用改进的Resnet18提取初始建筑特征.然后,设计显著区域抑制模块(salient re-gion suppression module,SRSM),通过隐藏最具判别性区域,引导网络学习潜在区域的特征,并设计多尺度特征融合网络(multi-scale feature fusion,MSFF),将多尺度结构与显著区域抑制相结合,以获取更完整的建筑元素特征.接着,利用通道注意力赋予各通道相应的权重,以突出重要的通道信息.最后,大边距度量损失函数(large-margin Softmax loss function,L-Softmax)通过最大化特征嵌入空间的决策边界,改善相似建筑风格的识别.在公共建筑数据集10类、25类及自建中国古建筑数据集上的实验结果表明,本文方法的准确率分别达到80.21％、64.4％和88.21％,其性能优于目前的先进方法.
Abstract
To address the problems of incomplete feature extraction of architectural elements and difficulties in the recognition of similar architectural styles,we propose a salient region suppression and multi-scale feature fusion(SRSMSFF)architectural style recognition method.First,the improved Resnet18 is used to extract the initial architectural features.Next,the salient region suppression module(SRSM)is designed,which guides the network to learn the features of potential regions by hiding the most discriminative regions.And multi-scale feature fusion(MSFF)is designed,which combines multi-scale structure with salient region suppression to obtain a more complete feature of architectural elements.Then,channel attention is used to assign corresponding weights to each channel,which can highlight important channel information.Finally,the large-margin Softmax loss function(L-Softmax)is introduced through maximizing the decision boundary distance of the feature embedding space,which improves the performance of similar architectural style recognition.The experimental results show that our model achieves 64.44％and 80.21％accuracy on the 25-class and 10-class public architectural style datasets.It achieves an accuracy of 88.21％on the dataset of ancient Chinese architectural styles.Its performance is superior to current advanced method.
关键词
图像处理/建筑风格/显著区域抑制/多尺度特征融合(MSFF)/大边距度量损失函数(L-Soft-max)Key words
image processing/architectural style/salient region suppression/multi-scale feature fusion(MSFF)/large-margin Softmax loss function(L-Softmax)引用本文复制引用
基金项目
国家自然科学基金面上项目(52278125)
陕西省重点研究计划项目(2021SF-429)
出版年
2024
NETL
NSTL
万方数据