ISEC: Iterative over-Segmentation via Edge Clustering
This addresses the efficiency and accuracy issues in superpixel-based segmentation for video processing, though it appears incremental as it builds on existing superpixel methods.
The paper tackled the over-segmentation problem in superpixel generation for image and video analysis by proposing ISEC, a method that iteratively clusters edges to produce adaptive superpixels, achieving a favorable balance between segmentation stability and accurate motion discontinuity representation at low computational cost.
Several image pattern recognition tasks rely on superpixel generation as a fundamental step. Image analysis based on superpixels facilitates domain-specific applications, also speeding up the overall processing time of the task. Recent superpixel methods have been designed to fit boundary adherence, usually regulating the size and shape of each superpixel in order to mitigate the occurrence of undersegmentation failures. Superpixel regularity and compactness sometimes imposes an excessive number of segments in the image, which ultimately decreases the efficiency of the final segmentation, specially in video segmentation. We propose here a novel method to generate superpixels, called iterative over-segmentation via edge clustering (ISEC), which addresses the over-segmentation problem from a different perspective in contrast to recent state-of-the-art approaches. ISEC iteratively clusters edges extracted from the image objects, providing adaptive superpixels in size, shape and quantity, while preserving suitable adherence to the real object boundaries. All this is achieved at a very low computational cost. Experiments show that ISEC stands out from existing methods, meeting a favorable balance between segmentation stability and accurate representation of motion discontinuities, which are features specially suitable to video segmentation.