We present a simple and efficient method based on deep learning to
automatically decompose sketched objects into semantically valid parts. We
train a deep neural network to transfer existing segmentations and labelings
from 3D models to freehand sketches without requiring numerous well-annotated
sketches as training data. The network takes the binary image of a sketched
object as input and produces a corresponding segmentation map with per-pixel
labelings as output. A subsequent post-process procedure with multi-label graph
cuts further refines the segmentation and labeling result. We validate our
proposed method on two sketch datasets. Experiments show that our method
outperforms the state-of-the-art method in terms of segmentation and labeling
accuracy and is significantly faster, enabling further integration in
interactive drawing systems. We demonstrate the efficiency of our method in a
sketch-based modeling application that automatically transforms input sketches
into 3D models by part assembly.

本研究提出了一种基于深度学习的简单高效方法，用于将手绘目标自动分解为语义上有效的部分，可实现快速的分割和标记。

深度学习快速素描分割和标注

Fast Sketch Segmentation and Labeling with Deep Learning

We present an accurate stereo matching method using local expansion moves
based on graph cuts. This new move-making scheme is used to efficiently infer
per-pixel 3D plane labels on a pairwise Markov random field (MRF) that
effectively combines recently proposed slanted patch matching and curvature
regularization terms. The local expansion moves are presented as many
alpha-expansions defined for small grid regions. The local expansion moves
extend traditional expansion moves by two ways: localization and spatial
propagation. By localization, we use different candidate alpha-labels according
to the locations of local alpha-expansions. By spatial propagation, we design
our local alpha-expansions to propagate currently assigned labels for nearby
regions. With this localization and spatial propagation, our method can
efficiently infer MRF models with a continuous label space using randomized
search. Our method has several advantages over previous approaches that are
based on fusion moves or belief propagation; it produces submodular moves
deriving a subproblem optimality; it helps find good, smooth, piecewise linear
disparity maps; it is suitable for parallelization; it can use cost-volume
filtering techniques for accelerating the matching cost computations. Even
using a simple pairwise MRF, our method is shown to have best performance in
the Middlebury stereo benchmark V2 and V3.

一篇关于使用基于图割的局部扩展方法的立体匹配技术研究，通过利用局部扩展移动进行实验，该算法提高了 3D 平面标签的准确性，并在 Middlebury 立体基准的 V2 和 V3 版本中表现出最佳性能。

使用本地扩展移动进行连续的三维标签立体匹配

Continuous 3D Label Stereo Matching using Local Expansion Moves

This is a survey of the method of graph cuts and its applications to graph
clustering of weighted unsigned and signed graphs. I provide a fairly thorough
treatment of the method of normalized graph cuts, a deeply original method due
to Shi and Malik, including complete proofs. The main thrust of this paper is
the method of normalized cuts. I give a detailed account for K = 2 clusters,
and also for K > 2 clusters, based on the work of Yu and Shi. I also show how
both graph drawing and normalized cut K-clustering can be easily generalized to
handle signed graphs, which are weighted graphs in which the weight matrix W
may have negative coefficients. Intuitively, negative coefficients indicate
distance or dissimilarity. The solution is to replace the degree matrix by the
matrix in which absolute values of the weights are used, and to replace the
Laplacian by the Laplacian with the new degree matrix of absolute values. As
far as I know, the generalization of K-way normalized clustering to signed
graphs is new. Finally, I show how the method of ratio cuts, in which a cut is
normalized by the size of the cluster rather than its volume, is just a special
case of normalized cuts.

本文系统地介绍了图割方法，着重介绍了归一化图割方法及其在加权无向图和有向图聚类中的应用，并提出了解决带符号图的问题的方法。此外，本文还表明了比率割方法是归一化割方法的特例。

无向和有向图的谱理论及其在图聚类中的应用：综述

Spectral Theory of Unsigned and Signed Graphs. Applications to Graph  Clustering: a Survey

We study an extension of the classical graph cut problem, wherein we replace
the modular (sum of edge weights) cost function by a submodular set function
defined over graph edges. Special cases of this problem have appeared in
different applications in signal processing, machine learning, and computer
vision. In this paper, we connect these applications via the generic
formulation of "cooperative graph cuts", for which we study complexity,
algorithms, and connections to polymatroidal network flows. Finally, we compare
the proposed algorithms empirically.

本文研究了一种基于子模集函数的图形切割问题，旨在连接信号处理、机器学习和计算机视觉等应用领域，最后通过算法比较进行了实证研究。