Share Email Print

Proceedings Paper

Genre-based image classification using ensemble learning for online flyers
Author(s): Payam Pourashraf; Noriko Tomuro; Emilia Apostolova
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents an image classification model developed to classify images embedded in commercial real estate flyers. It is a component in a larger, multimodal system which uses texts as well as images in the flyers to automatically classify them by the property types. The role of the image classifier in the system is to provide the genres of the embedded images (map, schematic drawing, aerial photo, etc.), which to be combined with the texts in the flyer to do the overall classification. In this work, we used an ensemble learning approach and developed a model where the outputs of an ensemble of support vector machines (SVMs) are combined by a k-nearest neighbor (KNN) classifier. In this model, the classifiers in the ensemble are strong classifiers, each of which is trained to predict a given/assigned genre. Not only is our model intuitive by taking advantage of the mutual distinctness of the image genres, it is also scalable. We tested the model using over 3000 images extracted from online real estate flyers. The result showed that our model outperformed the baseline classifiers by a large margin.

Paper Details

Date Published: 6 July 2015
PDF: 5 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310Z (6 July 2015); doi: 10.1117/12.2197138
Show Author Affiliations
Payam Pourashraf, DePaul Univ. (United States)
Noriko Tomuro, DePaul Univ. (United States)
Emilia Apostolova, BrokerSavant Inc. (United States)

Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top