Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
DynDex: a dynamic and non-metric space indexer
Proceedings of the tenth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
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 interactive approach for CBIR using a network of radial basis functions
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Aggregate similarity queries in relevance feedback methods for content-based image retrieval
Proceedings of the 2008 ACM symposium on Applied computing
Efficient RkNN retrieval with arbitrary non-metric similarity measures
Proceedings of the VLDB Endowment
Efficient reverse skyline retrieval with arbitrary non-metric similarity measures
Proceedings of the 14th International Conference on Extending Database Technology
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
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Over many years, almost all research work in the content-based image retrieval has used Minkowski distance (or Lp-norm) to measure similarity between images. However such functions cannot adequately capture the aspects of the characteristics of the human visual system. In this paper, we present a new similarity measure reflecting the nonlinearity of human perception. Based on this measure, we develop a similarity ranking algorithm for effective image retrieval. This algorithm exploits the inherent cluster structure revealed by an image dataset. Our method yields encouraging experimental results on a real image database and demonstrates its effectiveness.