Abstract
A number of image annotation information for a training image is available only at the image level, but not at the region level. However, an image contains several regions and each region may represent different semantic meaning. In this paper, a region based samples learning approach to image annotation using Gaussian mixture model (GMM) is presented, which make use of coordinate and color feature in image region to compute GMM of each region, by samples learning to achieve automatic annotation of each image semantic purpose. The experiments over Corel images have shown that this approach is effective for image annotation.
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Acknowledgments
This research was partially supported by “the Fundamental Research Funds for the Central Universities”.
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Luo, X., Kita, K. (2013). Region-Based Image Annotation Using Gaussian Mixture Model. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34531-9_53
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DOI: https://doi.org/10.1007/978-3-642-34531-9_53
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