Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Object of interest-based visual navigation, retrieval, and semantic content identification system
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
A novel multi-resolution video representation scheme based on kernel PCA
The Visual Computer: International Journal of Computer Graphics
Image retrieval based on indexing and relevance feedback
Pattern Recognition Letters
Image Retrieval by Elastic Matching of Shapes and Image Patterns
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
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In this paper we address a challenge of the problem of the dimensionality curse and the semantic gap reduction for content based image retrieval in large and heterogeneous databases. The strength of our idea resides in building an effective multidimensional indexing method based on kernel principal component analysis (KPCA) which supports efficiently similarity search of the heterogeneous vectors (color, texture, shape) and maps data vectors on a low feature space that is partitioned into regions. An efficient approach to approximate feature space regions is proposed with the corresponding upper and lower distance bounds. Finally, relevance feedback mechanism is exploited to create a flexible retrieval metric in order to reduce the semantic gap between the user need and the data representation. Experimental evaluations show that the use of region approximation approach with relevance feedback can significantly improve both the quality and the CPU time of the results.