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6 May 2019 Classification of real estate images using transfer learning
Yang Cao, Shinichi Nunoya, Yusuke Suzuki , Masachika Suzuki, Yoshio Asada, Hiroki Takahashi
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Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110691I (2019) https://doi.org/10.1117/12.2524417
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China
Abstract
With the growing mobility of the population and popularity of the Internet, real estate agents have larger database to manage. This paper presents a solution to classify images of a certain house, such as living room, kitchen, bathroom, layout sketch and external appearance collected by a real estate agent using transfer learning. The pictures are like those images posted on the real estate agent website to help people find out what’s the house looks like inside and outside. We employ a transfer learning approach for VGG-19 architecture. Using a network pre-trained on the general ImageNet dataset, we perform supervised fine-tuning on the last full connect layer and change the output size from 1000 to 5. Experimental results achieved with 5-fold cross-validation show that after training, this fine-tuning approach achieves high test accuracy of 99.4%.
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Yang Cao, Shinichi Nunoya, Yusuke Suzuki , Masachika Suzuki, Yoshio Asada, and Hiroki Takahashi "Classification of real estate images using transfer learning", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691I (6 May 2019); https://doi.org/10.1117/12.2524417
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KEYWORDS
Convolutional neural networks
Image classification
Data modeling
Neural networks
RGB color model
Computing systems
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