Foundations of statistical natural language processing
Foundations of statistical natural language processing
Self-Organizing Maps
Implementing Relevance Feedback as Convolutions of Local Neighborhoods on Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
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The distribution of a class of objects, such as images depicting a specific topic, can be studied by observing the best-matching units (BMUs) of the objects' feature vectors on a Self-Organizing Map (SOM). When the BMU "hits" on the map are summed up, the class distribution may be seen as a two-dimensional histogram or discrete probability density. Due to the SOM's topology preserving property, one is motivated to smooth the value field and spread out the values spatially to neighboring units, from where one may expect to find further similar objects. In this paper we study the impact of using more map units than just the single BMU of each feature vector in modeling the class distribution. We demonstrate that by varying the number of units selected in this way and varying the width of the spatial convolution one can find an optimal combination which maximizes the class detection performance.