PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps
Proceedings of the 14th annual ACM symposium on User interface software and technology
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
CAT: A Hierarchical Image Browser Using a Rectangle Packing Technique
IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
KissKissBan: a competitive human computation game for image annotation
Proceedings of the ACM SIGKDD Workshop on Human Computation
An interactive framework for image annotation through gaming
Proceedings of the international conference on Multimedia information retrieval
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Semantic annotation of image groups with self-organizing maps
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Efficient annotation of image data sets for computer vision applications
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Redundant dictionary spaces as a general concept for the analysis of non-vectorial data
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Efficient development of user-defined image recognition systems
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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Image recognition systems require large image data sets for the training process. The annotation of such data sets through users requires a lot of time and effort, and thereby presents the bottleneck in the development of recognition systems. In order to simplify the creation of image recognition systems it is necessary to develop interaction concepts for optimizing the usability of labeling systems. Semi-automatic approaches are capable of solving the labeling task by clustering the image data unsupervised and presenting this ordered set to a user for manual labeling. A labeling interface based on selforganizing maps (SOM) was developed and its usability was investigated in an extensive user study with 24 participants. The evaluation showed that SOM-based visualizations are suitable for speeding up the labeling process and simplifying the task for users. Based on the results of the user study, further concepts were developed to improve the usability.