ISEC: Iterative over-Segmentation via Edge Clustering

TL;DR

The paper introduces ISEC, a novel superpixel generation method that adaptively clusters edges to produce boundary-adherent, efficient superpixels suitable for video segmentation, with low computational cost.

Contribution

ISEC offers a new approach to superpixel generation by iteratively clustering edges, addressing over-segmentation and improving boundary adherence and efficiency.

Findings

ISEC produces adaptive superpixels with good boundary adherence.

ISEC achieves a favorable balance between stability and motion discontinuity representation.

ISEC operates at very low computational cost.

Abstract

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.