| Paper: | IMDSP-L5.1 | ||
| Session: | Image Segmentation | ||
| Time: | Wednesday, May 17, 14:00 - 14:20 | ||
| Presentation: | Lecture | ||
| Topic: | Image and Multidimensional Signal Processing: Image Segmentation | ||
| Title: | Homotopy-based Semi-Supervised Hidden Markov Tree for Texture Analysis | ||
| Authors: | Nilanjan Dasgupta, Shihao Ji, Lawrence Carin, Duke University, United States | ||
| Abstract: | A semi-supervised hidden Markov tree (HMT) model is developed for texture analysis, incorporating both labeled and unlabeled data for training; the optimal balance between labeled and unlabeled data is estimated via the homotopy method. In traditional EM-based semi-supervised modeling, this balance is dictated by the relative size of labeled and unlabeled data, often leading to poor performance. Semi-supervised modeling may be viewed as a source allocation problem between labeled and unlabeled data, controlled by a parameter ? ? [0, 1], where ? = 0 and 1 correspond to the purely supervised HMT model and purely unsupervised HMT-based clustering, respectively. We consider the homotopy method to track a path of fixed points starting from ? = 0, with the optimal source allocation identified as a critical transition point where the solution is unsupported by the initial labeled data. Experimental results on real textures demonstrate the superiority of this method compared to the EM-based semi-supervised HMT training. | ||
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