An unsupervised image segmentation algorithm based on the machine learning of appropriate features
This paper proposes a new approach to the feature based unsupervised image segmentation. The difficulty with the conventional unsupervised segmentation lies in finding appropriate features that discriminate a meaningful region from the others. In this paper, the appropriate features are automatically learnt by machine learning with boosting scheme. At the initial step, the image is split into many small regions (blocks at first) and strong classifiers for every region, which discriminate the region from the others, are found by AdaBoosting. Each strong classifier so obtained is the weighted sum of several popular weak classifiers (features), which best describes the coherence of the region and thus well discriminates the region from the others. The output of this classifier is used in designing the energy function for the labeling, in the form of Conditional Random Fields (CRFs). Minimization of the energy function produces the labeling result which reflects the property learnt by the classifier. For the labeling result, the machine learning is again performed and the process iterates until some conditions are met. Experimental results show that the proposed method provides competitive result compared to the conventional feature based methods.