Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
NETRA: a toolbox for navigating large image databases
NETRA: a toolbox for navigating large image databases
Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Image Classification and Retrieval Based on Wavelet-SOM
DANTE '99 Proceedings of the 1999 International Symposium on Database Applications in Non-Traditional Environments
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Array-index: a plug&search K nearest neighbors method for high-dimensional data
Data & Knowledge Engineering
| Hi-index | 0.00 |
Feature-based similarity retrieval became an important research issue in image database systems. The features of image data are useful in image discrimination. In this paper, we propose a fast k-Nearest Neighbor (k-NN) search algorithm for images clustered by the Self-Organizing Maps algorithm. Self-Organizing Maps (SOM) algorithm maps feature vectors from high dimensional feature space onto a two-dimensional space. The mapping preserves the topology (similarity) of the feature vectors by clustering mutually similar feature vectors in neighboring nodes (clusters). Our k-NN search algorithm utilizes the characteristics of these clusters to reduce the search space and thus speed up the search for exact k-NN answer images to a given query image. We conducted several experiments to evaluate the performance of the proposed algorithm using color feature vectors and obtained promising results.