A Transfer Learning Method with Multi-feature Calibration for Building Identification

Traditional building identification methods are difficult for extracting the specific information of various buildings. In this paper, A transfer learning method with multi-feature calibration is proposed for building identification. Our model is based on the pre-training and fine-tuning framework o...

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Bibliographic Details
Published in2020 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
Main Authors Mao, Jiafa, Yu, Linlin, Yu, Hui, Hu, Yahong, Sheng, Weiguo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Bottleneck Layer
Building Identification
Buildings
Calibration
Convolution
Convolutional neural networks
Feature extraction
Image recognition
Learning systems
Multi-Feature Calibration
Transfer Learning
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Summary:Traditional building identification methods are difficult for extracting the specific information of various buildings. In this paper, A transfer learning method with multi-feature calibration is proposed for building identification. Our model is based on the pre-training and fine-tuning framework of transfer learning. First, a CNN-based feature extractor, pre-trained by ImageNet, is adopted to extract features, then flatten the feature maps and feed it to a fully-connected network for image classification. This basic transfer learning model can correctly identify 81.2% of test samples. Further, a multi-feature calibration method is proposed. By defining the features of multi-functional buildings artificially, the feature vectors via the extractor are more representative and it can be efficiently applied on some small-sample data sets. We use a self-made building data set to test our methods. The experimental results show that the recognition accurate rate of the model with multi-feature calibration attains to 91.9%.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9207693