Efficient graph-based image segmentation via speeded-up turbo pixels

Abstract

An efficient graph based image segmentation algorithm exploiting a novel and fast turbo pixel extraction method is introduced. The images are modeled as weighted graphs whose nodes correspond to super pixels; and normalized cuts are utilized to obtain final segmentation. Utilizing super pixels provides an efficient and compact representation; the graph complexity decreases by hundreds in terms of node number. Connected K-means with convexity constraint is the key tool for the proposed super pixel extraction. Once the pixels are grouped into super pixels, iterative bi-partitioning of the weighted graph, as introduced in normalized cuts, is performed to obtain segmentation map. Supported by various experiments, the proposed two stage segmentation scheme can be considered to be one of the most efficient graph based segmentation algorithms providing high quality results.

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