| Semi-automatic acquisition and labelling of image data using SOMs (2008) | |||||||||||||||
Abstract | |||||||||||||||
| Abstract. Application of neural networks for real world object recognition suffers from the need to acquire large quantities of labelled image data. We propose a solution that acquires images from a domain at random and structures the data in two steps: Data driven mechanisms extract windows of interest, which are clustered by a SOM. Regions of the SOM in which objects form clusters serve as “suggestions ” for categories. An interactive visualisation of the SOM combined with distance measures allows the user to determine classes and build training sets. By this means, large labelled data sets for a neural classifier can be easily generated. 1 | |||||||||||||||
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