From Windows to Logos: Analyzing Outdoor Images to Aid Flyer Classification
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-93000-8_20
Reference17 articles.
1. Pourashraf, P., Tomuro, N., Apostolova, E.: Genre-based image classification using ensemble learning for online flyers. International Conference on Image Processing (ICDIP) (2015)
2. Apostolova, E., Tomuro, N.: Combining visual and textual features for information extraction from online flyers. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)
3. Pourashraf, P., Tomuro, N.: Use of a large image repository to enhance domain dataset for flyer classification. In: ISVC (2015)
4. Huiskes, M.J., Thomee, B., Lew, M.S.: New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. In: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 527–536. ACM, March 2010
5. Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 902–909. IEEE, June 2010
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