PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Principles of visual information retrieval
Principles of visual information retrieval
Self-Organizing Maps
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Optimal Combination of SOM Search in Best-Matching Units and Map Neighborhood
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Search engines for digital images using MSA
NOLASC'09 Proceedings of the 8th WSEAS international conference on Non-linear analysis, non-linear systems and chaos
An efficiency comparison of two content-based image retrieval systems, GIFT and PicSOM
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A SOM based model combination strategy
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
SOM-Based sample learning algorithm for relevance feedback in CBIR
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Expert Systems with Applications: An International Journal
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The Self-Organizing Map (SOM) can be used in implementingrelev ance feedback in an information retrieval system. In our approach, the map surface is convolved with a window function in order to spread the responses given by a human user for the seen data items. In this paper, a number of window functions with different sizes are compared in spreadingp ositive and negative relevance information on the SOM surfaces in an image retrieval application. In addition, a novel method for incorporatinglo cation-dependent information on the relative distances of the map units in the window function is presented.