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Use of a Large Image Repository to Enhance Domain Dataset for Flyer Classification

  • Payam PourashrafEmail author
  • Noriko Tomuro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

This paper describes our exploratory work on supplementing our dataset of images extracted from real estate flyers with images from a large general image repository to enhance the breadth of the samples and create a classification model which would perform well for totally unseen, new instances. We selected some images from the Scene UNderstanding (SUN) database which are annotated with the scene categories that seem to match with our flyer images, and added them to our flyer dataset. We ran a series of experiments with various configurations of flyer vs. SUN data mix. The results showed that the classification models trained with a mixture of SUN and flyer images produced comparable accuracies as the models trained solely with flyer images. This suggests that we were able to create a model which is scalable to unseen, new data without sacrificing the accuracy of the data at hand.

Keywords

Local Binary Pattern Property Type Scene Category Commercial Real Estate Scene Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.DePaul UniversityChicagoUSA

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