text: Superpixels provide an efficient low mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting "Superpixel Sampling Network" (SSN) is end-to-end trainable, which allows learning task-specific superpixels with flexible loss functions and has fast runtime. Extensive experimental analysis indicates that SSNs not only outperform existing superpixel algorithms on traditional segmentation benchmarks, but can also learn superpixels for other tasks. In addition, SSNs can be easily integrated into downstream deep networks resulting in performance improvements.
  @cite_24: We present in this paper a superpixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform superpixels with low computational costs. Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels. However, instead of using the traditional eigen-based algorithm, we approximate the similarity metric using a kernel function leading to an explicitly mapping of pixel values and coordinates into a high dimensional feature space. We revisit the conclusion that by appropriately weighting each point in this feature space, the objective functions of weighted K-means and normalized cuts share the same optimum point. As such, it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images. Experimental results show that LSC performs equally well or better than state of the art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation.
  @cite_28: Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions (e.g., with high intensity or color variation) and large superpixels in content-sparse regions. Rather than the conventional SLIC method that clusters pixels in R5, we map the image I to a 2-dimensional manifold M ⊂ R5, whose area elements are a good measure of the content density in I. We propose an efficient method to compute restricted centroidal Voronoi tessellation (RCVT) — a uniform tessellation — on M, which induces the content-sensitive superpixels in I. Unlike other algorithms that characterize content-sensitivity by geodesic distances, manifold SLIC tackles the problem by measuring areas of Voronoi cells on M, which can be computed at a very low cost. As a result, it runs 10 times faster than the state-of-the-art content-sensitive superpixels algorithm. We evaluate manifold SLIC and seven representative methods on the BSDS500 benchmark and observe that our method outperforms the existing methods.
  @cite_1: We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.
  @cite_25: 
summary: Clustering-based approaches, on the other hand, leverage traditional clustering techniques such as @math -means for superpixel segmentation. Widely-used algorithms in this category include SLIC @cite_25 , LSC @cite_24 , and Manifold-SLIC @cite_28 . These methods mainly do @math -means clustering but differ in their feature representation. While the SLIC @cite_25 represents each pixel as a @math -dimensional positional and color features ( @math features), LSC @cite_24 method projects these @math -dimensional features on to a @math -dimensional space and performs clustering in the projected space. Manifold-SLIC @cite_28 , on the other hand, uses a @math -dimensional manifold feature space for superpixel clustering. While these clustering algorithms require iterative updates, a non-iterative clustering scheme for superpixel segmentation is proposed in the SNIC method @cite_1 . The proposed approach is also a clustering-based approach. However, unlike existing techniques, we leverage deep networks to learn features for superpixel clustering via an end-to-end training framework.