Affinity Fusion Graph-based Framework for Natural Image Segmentation
This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels. These affinity nodes are selected based on a better affiliation of superpixels, namely subspace-preserving representation which is generated by sparse subspace clustering based on subspace pursuit. Then a KSC-graph is built via a novel kernel spectral clustering to explore the nonlinear relationships among these affinity nodes. Moreover, an adjacency-graph at each scale is constructed, which is further used to update the proposed KSC-graph at affinity nodes. The fusion graph is built across different scales, and it is partitioned to obtain final segmentation result. Experimental results on the Berkeley segmentation dataset and Microsoft Research Cambridge dataset show the superiority of our framework in comparison with the state-of-the-art methods. The code is available at https://github.com/Yangzhangcst/AF-graph.
Code (2)
Tasks
ClusteringImage SegmentationSegmentationSemantic SegmentationSuperpixelsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation
Affinity graph-based segmentation methods have become a major trend in computer vision. The performance of these methods relies on the constructed affinity graph, with particular emphasis on the neighborhood topology and…
Density EstimationImage SegmentationSemantic SegmentationSuperpixelsGo With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different defin…
SPARK: 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 partitioningDiffusion Processes for Retrieval Revisited
In this paper we revisit diffusion processes on affinity graphs for capturing the intrinsic manifold structure defined by pairwise affinity matrices. Such diffusion processes have already proved the ability to significan…
RetrievalSemi-supervised Learning on Graph with an Alternating Diffusion Process
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph. It is suboptimal to solve them independently,…
graph construction