Abstract
This paper describes a method that learns a variety of features to perform photo annotation. We introduce concept-specific regional features and combine them with global features. The regional features were extracted through a novel region selection algorithm based on Multiple Instance Learning. Supervised classification for photo annotation was learned using Support Vector Machines with extended Gaussian Kernels over the χ 2 distance, together with a simple greedy feature selection. The method was evaluated using the ImageCLEF 2009 Photo Annotation task and competitive benchmarking results were achieved.
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References
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vision 73(2), 213–238 (2007)
Ojala, T., Rautiainen, M., Matinmikko, E., Aittola, M.: Semantic image retrieval with hsv correlograms. In: 12th Scandinavian Conf. on Image Analysis, pp. 621–627 (2001)
Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: MULTIMEDIA 1996: Proceedings of the Fourth ACM International Conference on Multimedia, pp. 65–73. ACM, New York (1996)
Wu, J., Rehg, J.M.: Where am I: Place instance and category recognition using spatial pact. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)
Zhang, Q., Goldman, S.A.: Em-dd: An improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems, pp. 1073–1080. MIT Press, Cambridge (2001)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: Object localization by efficient subwindow search. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
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Ngiam, J., Goh, H. (2010). Learning Global and Regional Features for Photo Annotation. In: Peters, C., et al. Multilingual Information Access Evaluation II. Multimedia Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15751-6_36
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DOI: https://doi.org/10.1007/978-3-642-15751-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15750-9
Online ISBN: 978-3-642-15751-6
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