Abstract
We propose a region-based method for the annotation of outdoor photographs. First, images are oversegmented using the normalized cut algorithm. Each resulting region is described by color and texture features, and is then classified by a multi-class Support Vector Machine into seven classes: sky, vegetation, snow, water, ground, street, and sand. Finally, a rejection option is applied to discard those regions for which the classifier is not confident enough. For training and evaluation we used more than 12,000 images taken from the LabelMe project.
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References
Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)
Cheng, H., Wang, R.: Semantic modeling of natural scenes based on contextual Bayesian networks. Pattern Recognition 43(12), 4042–4054 (2010)
Ciocca, G., Cusano, C., Gasparini, F., Schettini, R.: Content aware image enhancement. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 686–697. Springer, Heidelberg (2007)
Cooper, T.: Color segmentation as an aid to white balancing for digital still cameras, 4300, 164–171 (2000)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proc. of Internet Imaging V. SPIE, vol. 5304, pp. 330–338 (2004)
Cusano, C., Gasparini, F., Schettini, R.: Image annotation for adaptive enhancement of uncalibrated color images. In: Bres, S., Laurini, R. (eds.) VISUAL 2005. LNCS, vol. 3736, pp. 216–225. Springer, Heidelberg (2006)
Fredembach, C., Estrada, F., Süsstrunk, S.: Memory colour segmentation and classification using class-specific eigenregions. Journal of the Society for Information Display 17(11), 921–931 (2009)
Gasparini, F., Schettini, R.: Color balancing of digital photos using simple image statistics. Pattern Recognition 37(6), 1201–1217 (2004)
Gijsenij, A., Gevers, T.: Color constancy using image regions. In: IEEE International Conference on Image Processing, vol. 3, pp. 501–504 (2007)
Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: IEEE 12th International Conference on Computer Vision, pp. 309–316 (2010)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 119–126 (2003)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43, 7–27 (2001)
Millet, C., Bloch, I., Hede, P., Moellic, P.: Using relative spatial relationships to improve individual region recognition. In: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technologies, EWIMT, vol. 5, pp. 119–126 (2005)
Ojala, T., Pietikäainen, M., Mäaenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Rui, X., Li, M., Li, Z., Ma, W., Yu, N.: Bipartite graph reinforcement model for web image annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 585–594 (2007)
Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and webbased tool for image annotation. International Journal of Computer Vision 77(1), 157–173 (2008)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Tsai, C., Hung, C.: Automatically annotating images with keywords: A review of image annotation systems. Recent Patents on Computer Science 1(1), 55–68 (2008)
Wang, C., Jing, F., Zhang, L., Zhang, H.: Content-based image annotation refinement. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Van de Weijer, J., Gevers, T., Bagdanov, A.: Boosting color saliency in image feature detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 150–156 (2006)
Wu, T., Lin, C., Weng, R.: Probability estimates for multi-class classification by pairwise coupling. The Journal of Machine Learning Research 5, 975–1005 (2004)
Yuan, J., Li, J., Zhang, B.: Exploiting spatial context constraints for automatic image region annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 595–604 (2007)
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Cusano, C. (2011). Region-Based Annotation of Digital Photographs. In: Schettini, R., Tominaga, S., Trémeau, A. (eds) Computational Color Imaging. CCIW 2011. Lecture Notes in Computer Science, vol 6626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20404-3_4
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DOI: https://doi.org/10.1007/978-3-642-20404-3_4
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