Automated Annotation of Landmark Images Using Community Contributed Datasets and Web Resources
A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. “A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris”. This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching offers 93.6% classification accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identifies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases.
Keywordsweb mining geo-tagged images landmark identification automated image captioning
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- 1.Geonames, http://www.geonames.org
- 2.Panoramio, http://www.panoramio.com
- 3.Yahoo! search boss, http://developer.yahoo.com/search/boss/
- 6.Cortes, C., Vapnik, V.: Support-vector networks, vol. (3), pp. 273–297 (1995)Google Scholar
- 7.Fritz, G., Seifert, C., Paletta, L.: A mobile vision system for urban detection with informative local descriptors. In: Proceedings of the IEEE International Conference on Computer Vision Systems (ICVS 2006), p. 30 (2006)Google Scholar
- 8.Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and evaluating tag recommenders in a live system. In: Workshop on Knowledge Discovery, Data Mining, and Machine Learning, pp. 44–51 (2009)Google Scholar
- 10.Lowe, D.G.: Local feature view clustering for 3D object recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-682–I-688 (2001)Google Scholar
- 12.Malobabic, J., le Borgne, H., Murphy, N., O’Connor, N.: Detecting the presence of large buildings in natural images. In: Proceedings of the 4th International Workshop on Content-Based Multimedia Indexing (CBMI 2005), pp. 529–532 (2005)Google Scholar
- 14.Qingji, G., Juan, L., Guoqing, Y.: Vision based road crossing scene recognition for robot localization. In: Proceedings of the International Conference on Computer Science and Software Engineering, vol. 6, pp. 62–66 (2008)Google Scholar
- 17.Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51 (1998)Google Scholar
- 18.van Rijsbergen, C.: Information Retrieval, 2nd edn., Butterworths (1979)Google Scholar
- 19.Yeh, T., Tollmar, K., Darrell, T.: Searching the web with mobile images for location recognition. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. 2, pp. 76–81 (2004)Google Scholar