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
Formulating context-dependent similarity functions
Proceedings of the 13th annual ACM international conference on Multimedia
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Over many years, almost all research work in the content-based image retrieval (CBIR) has used Minkowski metric (or Lp-norm) to measure similarity between images. However, those functions cannot adequately capture the nonlinear relationships in contextual information given by image datasets. In this paper, we present a new similarity measure reflecting the nonlinearity of contextual information. Moreover, we propose a new similarity ranking algorithm based on this similarity measure for effective CBIR. Our algorithm yields superior experimental results on real image database and demonstrates its effectiveness.