Real Estate Image Classification

Jawadul H. Bappy, Joseph Barr, Narayanan Nani Srinivasan, Amit K. Roy-Chowdhury

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review


    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 languageEnglish
    Title of host publicationWinter Conference on Applications of Computer Vision (WACV)
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages9
    ISBN (Electronic)9781509048229
    ISBN (Print)9781509048236
    Publication statusPublished - 2017
    Event2017 IEEE Winter Conference on Applications of Computer Vision (WACV) - Santa Rosa, United States
    Duration: 24 Mar 201731 Mar 2017
    Conference number: 16881594


    Conference2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
    Country/TerritoryUnited States
    Internet address


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