Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Font Adaptive Word Indexing of Modern Printed Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Embedded Map Projection for Dimensionality Reduction-Based Similarity Search
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Efficient word retrieval by means of SOM clustering and PCA
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
We describe a dimensionality reduction method used to perform similarity search that is tested on document image retrieval applications. The approach is based on data point projection into a low dimensional space obtained by merging together the layers of a Growing Hierarchical Self Organizing Map (GHSOM) trained to model the distribution of objects to be indexed. The low dimensional space is defined by embedding the GHSOM sub-maps in the space defined by a non-linear mapping of neurons belonging to the first level map. The latter mapping is computed with the Sammon projection algorithm. The dimensionality reduction is used in a similarity search framework whose aim is to efficiently retrieve similar objects on the basis of the distance among projected points corresponding to high dimensional feature vectors describing the indexed objects. We compare the proposed method with other dimensionality reduction techniques by evaluating the retrieval performance on three datasets.