Semi-automatic acquisition and labelling of image data using SOMs

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.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Gunther Heidemann, Axel Saalbach, Helge Ritter
• Keywords: Data Driven Mechanisms, ESANN 2003, ESANN 2007, Labelled Image Data, Large Labelled Data