Semi-Supervised Normalized Cuts for Image Segmentation
Since its introduction as a powerful graph-based method for image segmentation, the Normalized Cuts (NCuts) algorithm has been generalized to incorporate expert knowledge about how certain pixels or regions should be grouped, or how the resulting segmentation should be biased to be correlated with priors. Previous approaches incorporate hard must-link constraints on how certain pixels should be grouped as well as hard cannot-link constraints on how other pixels should be separated into different groups. In this paper, we reformulate NCuts to allow both sets of constraints to be handled in a soft manner, enabling the user to tune the degree to which the constraints are satisfied. An approximate spectral solution to the reformulated problem exists without requiring explicit construction of a large, dense matrix; hence, computation time is comparable to that of unconstrained NCuts. Using synthetic data and real imagery, we show that soft handling of constraints yields better results than unconstrained NCuts and enables more robust clustering and segmentation than is possible when the constraints are strictly enforced.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringImage SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
A Sparse Graph Formulation for Efficient Spectral Image Segmentation
Spectral Clustering is one of the most traditional methods to solve segmentation problems. Based on Normalized Cuts, it aims at partitioning an image using an objective function defined by a graph. Despite their mathemat…
Image SegmentationSegmentationSemantic SegmentationConsensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationUnsupervised Segmentation by Diffusing, Walking and Cutting
We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationSuperpixel Segmentation Using Linear Spectral Clustering
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 …
ClusteringImage SegmentationSegmentationSemantic Segmentation+1Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice
The minimum graph cut and minimum $s$-$t$-cut problems are important primitives in the modeling of combinatorial problems in computer science, including in computer vision and machine learning. Some of the most efficient…
Gaussian ProcessesImage SegmentationSemantic Segmentation