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 mathematical attractiveness, spectral approaches are traditionally neglected by the scientific community due to their practical issues and underperformance. In this paper, we adopt a sparse graph formulation based on the inclusion of extra nodes to a simple grid graph. While the grid encodes the pixel spatial disposition, the extra nodes account for the pixel color data. Applying the original Normalized Cuts algorithm to this graph leads to a simple and scalable method for spectral image segmentation, with an interpretable solution. Our experiments also demonstrate that our proposed methodology over performs both traditional and modern unsupervised algorithms for segmentation in both real and synthetic data.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification
Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption in subspace clustering is that the samples belonging to different clusters/segments lie in separable subsp…
ClusteringDictionary LearningHyperspectral Image Classificationhyperspectral image clustering+6SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-derived affinities. Such approaches, based …
Semantic Segmentationgraph partitioningSpectral Image Segmentation with Global Appearance Modeling
We introduce a new spectral method for image segmentation that incorporates long range relationships for global appearance modeling. The approach combines two different graphs, one is a sparse graph that captures spatial…
Image SegmentationSemantic SegmentationMineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks
We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. In most cases, Backscattered Electron (BSE) images obtained using SEM do…
Graph Neural NetworkSegmentationSuperpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. Since it is likely to find spectrally similar pixels in a …
Hyperspectral UnmixingSegmentationSuperpixels