SEEDS: Superpixels Extracted via Energy-Driven Sampling

Abstract

Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color homogeneity. The optimization is accomplished by sophisticated methods that progressively build the superpixels, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We introduce a new approach based on a simple hill-climbing optimization. Starting from an initial superpixel partitioning, it continuously refines the superpixels by modifying the boundaries. We define a robust and fast to evaluate energy function, based on enforcing color similarity between the boundaries and the superpixel color histogram. In a series of experiments, we show that we achieve an excellent compromise between accuracy and efficiency. We are able to achieve a performance comparable to the state-of-the-art, but in real-time on a single Intel i7 CPU at 2.8 GHz.

Superpixels: the end of pixels in OBIA: A comparison of state-of-the-art superpixel methods for remote sensing data

“…For this study, we compared 4 state-of-the-art superpixel algorithms, namely Simple Linear Iterative Clustering (SLIC and SLICO -parameter free) (Achanta et al, 2012), Superpixels Extracted via Energy-Driven Sampling (SEEDS) (Van den Bergh et al, 2012) and Linear Spectral Clustering (LSC) (Li and Chen, 2015). In computer vision, the 4 algorithms were found to be very efficient and accurate, outperforming many existing algorithms (Achanta et al, 2012;Van den Bergh et al, 2012;Li and Chen, 2015). For derivation of superpixels, we have used the open-source GDAL implementation, available on https://github.com/cbalint13/gdalsegment.…”

“…SEEDS algorithm is a simple hill-climbing optimization which starts from an initial superpixel partitioning and continuously refines the superpixels by modifying the boundaries (Van den Bergh et al, 2012). The algorithm is based on a robust and fast to evaluate energy function, based on enforcing color similarity between the boundaries and the superpixel color histogram ( Van den Bergh et al, 2012).…”

Mapping-Based Image Diffusion

“…It is not until recently these partitioning methods are efficient enough to produce sufficiently accurate regions for image denoising while achieving computation times suitable for practical applications. One oversegmentation method that satisfy these demands is SEEDS, superpixels extracted via energy-driven sampling [9]. With the assumption that each segment in the oversegmentation map is independent, we estimate local second order statistics for each segment.…”

API-Constrained Genetic Improvement

“…In the next two sections we apply GI to just the C++ source code which implements the SEEDS picture segmentation [18]. This implementation won the State of the Art Vision Challenge (http://code.opencv.org/projects/opencv/ wiki/VisionChallenge) last year at the 28 th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) and was subsequently incorporated into the Open Source Computer Vision (OpenCV) library.…”