Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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We describe a clustering approach with the emphasis on detecting coherent structures in a complex dataset, and illustrate its effectiveness with computer vision applications. By complex data, we mean that the attribute variations among the data are too extensive such that clustering based on a single feature representation/descriptor is insufficient to faithfully divide the data into meaningful groups. The proposed method thus assumes the data are represented with various feature representations, and aims to uncover the underlying cluster structure. To that end, we associate each cluster with a boosting classifier derived from multiple kernel learning, and apply the cluster-specific classifier to feature selection across various descriptors to best separate data of the cluster from the rest. Specifically, we integrate the multiple, correlative training tasks of the cluster-specific classifiers into the clustering procedure, and cast them as a joint constrained optimization problem. Through the optimization iterations, the cluster structure is gradually revealed by these classifiers, while their discriminant power to capture similar data would be progressively improved owing to better data labeling.