Abstract
In this paper, we propose to exploit geotags as additional information for visual recognition of consumer photos to improve its performance. Geotags, which represent places where the photos were taken, for photos can be obtained automatically by carrying a portable small GPS device with digital cameras. Geotags have potential to improve performance of visual image recognition, since recognition targets are unevenly distributed. For example, “beach” photos can be taken near the sea and “lion” photos can be taken only in a zoo except Africa.
To integrate geotag information into visual image recognition, we adopt two types of geographical information, raw values of latitude and longitude, and visual feature of aerial photos around the location the geotag represents. As classifiers, we use both a discriminative method and a generative method in the experiments.
The objective of this paper is to examine if geotags can help category-level image recognition. Note that we define an image recognition problem as deciding if an image is associated with a certain given concept such as “mountain” and “beach” in this paper. We propose a novel method to carry out geotagged image recognition in this paper. The experimental results demonstrate effectiveness of usage of geographical information for recognition of consumer photos.
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Keywords
- Support Vector Machine
- Aerial Photo
- Visual Feature
- Latent Dirichlet Allocation
- Scale Invariant Feature Transform
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.
References
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 59–74 (2004)
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: Proc. of IEEE Computer Vision and Pattern Recognition, pp. 524–531 (2005)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 43, 177–196 (2001)
Kennedy, L., Naaman, M.: Generating diverse and representative image search results for landmarks. In: Proc. of the International World Wide Web Conference, pp. 297–306 (2008)
Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote sensing and image interpretation. John Wiley, Chichester (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Monay, F., Gatica-Perez, D.: Modeling semantic aspects for cross-media image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1802–1817 (2007)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their localization in images. In: Proc. of IEEE International Conference on Computer Vision, pp. 370–377 (2005)
Snavely, N., Seitz, S., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Transactions on Graphics (TOG) 25(3), 835–846 (2006)
Varma, M., Ray, D.: Learning the discriminative power-invariance trade-off. In: Proc. of IEEE International Conference on Computer Vision, pp. 1150–1157 (2007)
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Yaegashi, K., Yanai, K. (2009). Can Geotags Help Image Recognition?. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_32
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DOI: https://doi.org/10.1007/978-3-540-92957-4_32
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