A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Segmentation of Color Images Based on k -means Clustering in the Chromaticity Plane
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Analysis of natural scenes.
Image Segmentation by Unsupervised Sparse Clustering
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Area Segmentation of Images Using Edge Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting and Labeling Boundary Segments in Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A robust approach to segment desired object based on salient colors
Journal on Image and Video Processing - Color in Image and Video Processing
Image segmentation using automatic seeded region growing and instance-based learning
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Multi-level Low-rank Approximation-based Spectral Clustering for image segmentation
Pattern Recognition Letters
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In this paper, we present a novel solution for image segmentation based on positiveness which regards the segmentation as a graph-theoretic clustering problem. Contrary to spectral clustering methods using eigenvectors, the proposed method tries to find an additive combination of positive components from an originally positive data-driven matrix. By using the positiveness constraint, we obtain sparsely clustered results which do not include cancellations by negative entries. Thus, we call this method sparse clustering. The proposed method adopts a binary tree structure and solves a model selection problem by automatically determining the number of clusters using intra- and inter-cluster measures. We tested our method with image sequences as well as single frame data such as points and gray-scale, color, and texture images. Moreover, in order to objectively evaluate the performance of our method, we compared the results of the proposed method with those of the human segmentation and the Ncut method using various images including the Berkeley datasets. Experimental results show that the proposed method provides very successful and encouraging segmentations.