Combinatorial optimization: algorithms and complexity
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Introduction to statistical pattern recognition (2nd ed.)
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Automatic Classification of Single Facial Images
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Normalized Cuts and Image Segmentation
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From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
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Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Multiclass Spectral Clustering
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel k-means: spectral clustering and normalized cuts
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The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
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Two-scale image retrieval with significant meta-information feedback
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Discriminative cluster analysis
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Real-time computerized annotation of pictures
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Adaptive dimension reduction using discriminant analysis and K-means clustering
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Label Propagation through Linear Neighborhoods
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Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Generating diverse and representative image search results for landmarks
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SS-ClusterTree: a subspace clustering based indexing algorithm over high-dimensional image features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Annotating personal albums via web mining
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Ranking with local regression and global alignment for cross media retrieval
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Similarity-based classification of sequences using hidden Markov models
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CLUE: cluster-based retrieval of images by unsupervised learning
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Unsupervised image-set clustering using an information theoretic framework
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Hidden Markov Model-Based Weighted Likelihood Discriminant for 2-D Shape Classification
IEEE Transactions on Image Processing
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Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Face and Human Gait Recognition Using Image-to-Class Distance
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Improved MinMax cut graph clustering with nonnegative relaxation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Mining multi-tag association for image tagging
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VisionGo: Towards video retrieval with joint exploration of human and computer
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Beyond search: Event-driven summarization for web videos
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Tag-based social image search with visual-text joint hypergraph learning
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Intelligent photo clustering with user interaction and distance metric learning
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l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Local image tagging via graph regularized joint group sparsity
Pattern Recognition
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis
Information Sciences: an International Journal
Multimedia encyclopedia construction by mining web knowledge
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Recognizing architecture styles by hierarchical sparse coding of blocklets
Information Sciences: an International Journal
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
Rapid blockwise multi-resolution clustering of facial images for intelligent watermarking
Machine Vision and Applications
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In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data points. Inspired by the Fisher criterion, we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique. To obtain the clustering result, we further propose a unified objective function to globally integrate the local models of all the local cliques. With the unified objective function, spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples. We show that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). In contrast to NCut in which the Laplacian matrix is directly calculated based upon a Gaussian function, a new Laplacian matrix is learnt in LDMGI by exploiting both manifold structure and local discriminant information. We also prove that K-means and discriminative K-means (DisKmeans) are both special cases of LDMGI. Extensive experiments on several benchmark image datasets demonstrate the effectiveness of LDMGI. We observe in the experiments that LDMGI is more robust to algorithmic parameter, when compared with NCut. Thus, LDMGI is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters.