Abstract
In selective object segmentation, the goal is to extract the entire object of interest without regards to homogeneous regions or object shape. In this paper we present the selective image segmentation problem as a classification problem, and use active learning to train an image feature classifier to identify the object of interest. Since our formulation of this segmentation problem uses human interaction, active learning is used for training to minimize the training effort needed to segment the object. Results using several images with known ground truth are presented to show the efficacy of our approach for segmenting the object of interest in still images. The approach has potential applications in medical image segmentation and content-based image retrieval among others.
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References
Swain, C., Chen, T.: Defocus-based image segmentation. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 1995, pp. 2403–2406 (1995)
Harville, M., Gordon, G.G., Woodfill, J.: Foreground segmentation using adaptive mixture models in color and depth. In: IEEE Workshop on Detection and Recognition of Events in Video, pp. 3–11 (2001)
Stalling, D., Hege, H.-C.: Intelligent scissors for medical image segmentation. In: Arnolds, B., Müller, H., Saupe, D., Tolxdorff, T. (eds.) Proceedings of 4th Freiburger Workshop Digitale Bildverarbeitung in der Medizin, Freiburg, pp. 32–36 (1996)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive image segmentation using an adaptive GMMRF model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004)
Li, M., Sethi, I.K.: SVM-based classifier design with controlled confidence. In: Proceedings of 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge, UK, vol. 1, pp. 164–167 (2004)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Li, M., Sethi, I.K.: New online learning algorithm with application to image segmentation. In: Proc. Electronic Imaging, Image Processing: Algorithms and Systems IV, vol. 5672, SPIE (2005)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001)
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Ma, A., Patel, N., Li, M., Sethi, I.K. (2006). Confidence Based Active Learning for Whole Object Image Segmentation. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_99
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DOI: https://doi.org/10.1007/11848035_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
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