Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Kernel principal component analysis for content based image retrieval
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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In this paper, we propose a new nonlinear paradigm to clustering, indexing and searching for content-based image retrieval (CBIR). The scheme is designed for approximate searches and all the work is performed in a transformed feature space. We first (1) map the input space into a feature space via a nonlinear map, (2) compute the top eigenvectors in that feature space, and (3) capture cluster structure based on the eigenvectors. We (4) describe each cluster with a minimal hypersphere containing all objects in the cluster, (5) derive the similarity measure for each cluster individually and (6) construct a bitmap index for each cluster. Finally we (7) model the similarity query as a hyper-rectangular range query and search the clusters near the query point. Our preliminary experimental results for our new framework demonstrate considerable effectiveness and efficiency in CBIR.