Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying the extended mass-constraint EM algorithm to image retrieval
Computers & Mathematics with Applications
Minimum Bayes error features for visual recognition
Image and Vision Computing
Image retrieval using query by contextual example
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A new relevance feedback technique for iconic image retrieval based on spatial relationships
Journal of Systems and Software
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
IPSILON: incremental parsing for semantic indexing of latent concepts
IEEE Transactions on Image Processing
A new approach to cross-modal multimedia retrieval
Proceedings of the international conference on Multimedia
A review on automatic image annotation techniques
Pattern Recognition
On signal representations within the Bayes decision framework
Pattern Recognition
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Effective heterogeneous similarity measure with nearest neighbors for cross-media retrieval
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
| Hi-index | 35.69 |
We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower- and upper-bounded by functions of the Bayes and density estimation errors, and the impact of the components of the retrieval architecture (namely, the feature transformation and density estimation) on these bounds is characterized. This characterization suggests interpreting the search for the MPE feature set as the search for the minimum of the convex hull of a collection of curves of probability of error versus feature space dimension. A new algorithm for MPE feature design, based on a dictionary of empirical feature sets and the wrapper model for feature selection, is proposed. It is shown that, unlike traditional feature selection techniques, this algorithm scales to problems containing large numbers of classes. Experimental evaluation reveals that the MPE architecture is at least as good as popular empirical solutions on the narrow domains where these perform best but significantly outperforms them outside these domains.