Abstract
This article proposed a progressive image segmentation, which allow users to segment images according to their preferences without any boring pre-labeling or training stages. We use an online learning method to train/update the segmentation model progressively. User can scribble on the image to label initial samples or correct the false-labeled regions of the result. To efficiently integrate the interaction with the learning and updating process, a three-level representation of images is built. The proposed method has three advantages. Firstly, the segmentation model can be learned online along with user’s manipulation without any pre-labeling. Secondly, the diversity of segmentation accord with user’s preferences can be met flexibly, and the more use the more accurate the segmentation could be. Finally, the segmentation model can be updated online to meet the needs of users. The experimental results demonstrate these advantages.
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Acknowledgments
This work is supported by the National High Technology Research and Development Program of China (Project No. 2007AA01Z334), National Natural Science Foundation of China (Project No. 61321491 and 61272219), Innovation Fund of State Key Laboratory for Novel Software Technology (Project No. ZZKT2013A12).
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Hu, J., Sun, Z., Yang, K., Chen, Y. (2015). Progressive Image Segmentation Using Online Learning. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_18
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DOI: https://doi.org/10.1007/978-3-319-24075-6_18
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