Guided interactive image segmentation using machine learning and color based data set clustering
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We present a novel approach that combines machine learning based interactive image segmentation with a two-stage clustering method for identification of similarly colored images enabling efficient batch image segmentation through guided reuse of interactively trained classifiers. The segmentation task is formulated as a supervised machine learning problem working on supervoxels. These visually homogeneous groups of voxels are characterized using local color, edge and texture features. Classifiers are interactively trained from sparse annotations in a iterative process of annotation refinement. Resulting models can be used for batch processing of previously unseen images. However, due to systemic discrepancies of image colorization classifier reusability is typically limited. By clustering a set of images into subsets of similar colorization, considering characteristic dominant color vectors obtained from the individual images it is possible to identify a minimal set of prototype images eligible for interactive segmentation. We demonstrate that limiting the reuse of pre-trained classifiers to images in the same color-cluster significantly improves the average segmentation performance of batch processing. The described methods are implemented in our free image processing and quantification software TiQuant released alongside this publication.
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