Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
SIAM Journal on Computing
SIAM Journal on Computing
Block-quantized kernel matrix for fast spectral embedding
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fast and exact out-of-core and distributed k-means clustering
Knowledge and Information Systems
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Image segmentation by unsupervised sparse clustering
Pattern Recognition Letters
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
Less is More: Sparse Graph Mining with Compact Matrix Decomposition
Statistical Analysis and Data Mining
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Colibri: fast mining of large static and dynamic graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Approximate Spectral Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local information-based fast approximate spectral clustering
Pattern Recognition Letters
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Spectral clustering is a well-known graph-theoretic approach of finding natural groupings in a given dataset, and has been broadly used in image segmentation. Nowadays, High-Definition (HD) images are widely used in television broadcasting and movies. Segmenting these high resolution images presents a grand challenge to the current spectral clustering techniques. In this paper, we propose an efficient spectral method, Multi-level Low-rank Approximation-based Spectral Clustering (MLASC), to segment high resolution images. By integrating multi-level low-rank matrix approximations, i.e., the approximations to the affinity matrix and its subspace, as well as those for the Laplacian matrix and the Laplacian subspace, MLASC gains great computational and spacial efficiency. In addition, the proposed fast sampling strategy make it possible to select sufficient data samples in MLASC, leading to accurate approximation and segmentation. From a theoretical perspective, we mathematically prove the correctness of MLASC, and provide detailed analysis on its computational complexity. Through experiments performed on both synthetic and real datasets, we demonstrate the superior performance of MLASC.