The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A New Indexing Scheme for Content-Based Image Retrieval
Multimedia Tools and Applications
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
An efficient indexing method for nearest neighbor searches inhigh-dirnensional image databases
IEEE Transactions on Multimedia
2009 Special Issue: RKF-PCA: Robust kernel fuzzy PCA
Neural Networks
Robust kernel PCA using fuzzy membership
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Content based image retrieval based on a nonlinear similarity model
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Who is repinning?: predicting a brand's user interactions using social media retrieval
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
| Hi-index | 0.00 |
Kernel principal component analysis (PCA) has recently been proposed as a nonlinear extension of PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This paper illustrates the potential of kernel PCA for dimensionality reduction and feature extraction in content-based image retrieval. By the use of Gaussian kernels, the principal components were computed in the feature space of an image data set and they are used as new dimensions to approximate images. Extensive experimental results show that kernel PCA performs better than linear PCA in content-based image retrievals.