Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
Spectral structuring of home videos
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Deriving semantics for image clustering from accumulated user feedbacks
Proceedings of the 15th international conference on Multimedia
Image co-clustering with multi-modality features and user feedbacks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Supervised manifold learning for image and video classification
Proceedings of the international conference on Multimedia
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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In this paper, we present a novel spectral analysis algorithm for image clustering. First, the image manifold is embedded onto a low-dimensional feature space with dual objectives, i.e., maximizing the distances of faraway sample pairs meanwhile preserving the local manifold structure, which essentially results in a Trace Ratio optimization problem. Then an efficient iterative procedure is proposed to directly optimize the trace ratio and finally the clustering process is implemented on the derived low-dimensional embedding. Moreover, the linear approximation is also presented for handling the out-of-sample data. Experimental results show that our algorithm, referred to as Maximum Unfolded Embedding, brings an encouraging improvement in clustering accuracy over the state-of-the-art algorithms, such as K-Means, PCA-Kmeans, normalized cut \cite shi00normalized, and Locality Preserving Clustering [13].