Abstract
Posting pictures is a necessary part of advertising a home for sale. Agents typically sort through dozens of images from which to pick the most complimentary ones. This is a manual effort involving annotating images accompanied by descriptions (bedroom, bathroom, attic, etc.). When volumes are small, manual annotation is not a problem, but there is a point where this becomes too burdensome and ultimately infeasible. Here, we propose an approach based on computer vision methodology to radically increase the efficiency of such tasks. We present a high-confidence image classification framework, whose inputs are images and outputs are labels. The core of the classification algorithm is long short term memory (LSTM), and fully connected neural networks, along with a substantial preprocessing using 'contrast-limited adaptive histogram equalization (CLAHE) for image enhancement. Since, there is no standard benchmark containing a comprehensive dataset of well-annotated real estate images, we introduce Real Estate Image (REI) database for evaluating the image classification algorithms. Therein we demonstrate empirics based on our proposed framework on the new REI dataset, as well as on the SUN dataset.
Original language | English |
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Title of host publication | Winter Conference on Applications of Computer Vision (WACV) |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 373-381 |
Number of pages | 9 |
ISBN (Electronic) | 9781509048229 |
ISBN (Print) | 9781509048236 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) - Santa Rosa, United States Duration: 24 Mar 2017 → 31 Mar 2017 Conference number: 16881594 http://pamitc.org/wacv2017/ |
Conference
Conference | 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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Country/Territory | United States |
Period | 24/03/17 → 31/03/17 |
Internet address |