Floating search methods in feature selection
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Algorithm Design
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Global Geometric Approach for Image Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A survey of kernel and spectral methods for clustering
Pattern Recognition
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, for efficient clustering of visual image data that have arbitrary mixture distributions, we propose a simple distance metric learning method called Maximum Normalized Spacing (MNS) which is a generalized principle based on Maximum Spacing [12] and Minimum Spanning Tree (MST). The proposed Normalized Spacing (NS) can be viewed as a kind of adaptive distance metric for contextual dissimilarity measure which takes into account the local distribution of the data vectors. Image clustering is a difficult task because there are multiple nonlinear manifolds embedded in the data space. Many of the existing clustering methods often fail to learn the whole structure of the multiple manifolds and they are usually not very effective. Combining both the internal and external statistics of clusters to capture the density structure of manifolds, MNS is capable of efficient and effective solving the clustering problem for the complex multi-manifold datasets in arbitrary metric spaces. We apply this MNS method into the practical problem of multi-view image clustering and obtain good results which are helpful for image browsing systems. Using the COIL-20 [19] and COIL-100 [18] multi-view image databases, our experimental results demonstrate the effectiveness of the proposed MNS clustering method and this clustering method is more efficient than the traditional clustering methods.