# A Two-Stage Gradient Ascent-Based Superpixel Framework for Adaptive Segmentation
## 1 Introduction
Most superpixel segmentation methods cannot become practical because of their high complexity and memory requirements. In recent years, advanced deep learning applications and excellent structural improvements are proposed to make superpixel algorithms more outstanding. [PERSON] et al. [17] acquire homogeneous change samples from Synthetic Aperture Radar (SAR) images by SLIC, which are then fed to a feature learning method based on the stacked Contractive AutoEncoder (sCAE) to learn the features for change detection. [PERSON] et al. [18] propose a novel fully supervised scheme for semantic segmentation based on LSC superpixels, which utilizes the advantage of adaptive representation of superpixels context by inferring superpixel-based continuous Conditional Random Field (C-CRF) on features of full resolution. [PERSON] et al. [19] introduce the non-stationarity measure into distance measure and propose nSLIC, which is variable in accordance with local image feature and eliminate the compactness parameter, and eventually improves the visual performance and computing efficiency. To address the segmentation problem of generating structure-sensitive superpixel (SSS) [20], Manifold SLIC (MSLIC) [21] represents the input image as a 2-dimensional manifold, whose area elements are a good measure of content density. SSS are then achieved by computing a Restricted Centroidal Voronoi Tessellation (RCVT) on the manifold, which can be computed with very little cost. What's more, instead of inventing new algorithms, some NVIDIA CUDA-based GPU implementations of state-of-the-art superpixel algorithms are proposed, such as gSLIC [22] and gLSC [23], which modify the original structures thus making them more suitable to parallel deployment and faster-than-real-time application. It is also worth noting that the watershed runs in \(O(N\log N)\), whereas it can be performed with high efficiency empirically. In addition, a critical provision for better segmentation performance is to determine the seed in advance [24]. [PERSON] et.al [25] propose a morphological reconstruction-based approach to identify collection basin markers, and effectively improve the accuracy of froth image segmentation. [PERSON] et.al [26] introduce spatial constraint and edge-preserving to a SLIC-like grid scheme to generate uniform watershed superpixel, which offers controllability on superpixel number and their compactness. In practice, trade-offs almost always have to be made that should balance some characteristics of segmentation algorithm, so that it can significantly optimize one aspect with slight decrease of another. In this paper, a two-stage image segmentation framework is proposed, which combines two gradient ascent methods with a coarse-to-fine partition strategy. Firstly, the image is partitioned to regular superpixels by a novel speeded-up SLIC (sSLIC) with emphasis on time efficiency. Based on the calculation of sSLIC, a homogeneity criterion is put forward to define under-segmentation on all sSLIC superpixels. An adaptive marker-controlled watershed algorithm is then proposed to subdivide the misclassified pixels in every heterogeneous superpixel region. Finally, after two-times trade-offs between runtime and accuracy, the framework achieves a better overall performance. The rest of this paper is organized as follows. Section 2 presents a preliminary on the conventional SLIC and watershed algorithm. Section 3 explicates the proposed two-stage segmentation framework in detail. Qualitative and quantitative analysis are presented in Section 4, Section 5 gives the conclusions.