Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Neural Networks
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Large-Scale Real-Time Object Identification Based on Analytic Features
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Expert Systems with Applications: An International Journal
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
Prototype-based classification of dissimilarity data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Relational extensions of learning vector quantization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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We address the problem of fast figure-ground segmentation of single objects from cluttered backgrounds to improve object learning and recognition. For the segmentation, we use an initial foreground hypothesis to train a classifier for figure and ground on topographically ordered feature maps with generalized learning vector quantization. We investigate the contribution of several adaptive metrics to enable generalization to the main object parts and derive a foreground classification, which yields an improved bottom-up hypothesis. We show that metrics adaptation is a powerful enrichment, where generalizing the Euclidean metrics towards local matrices of relevance factors leads to a higher classification accuracy and considerable robustness on partially inconsistent supervised information. Additionally, we verify our results in an online learning scenario and show that figure-ground segregation using this adaptive metrics enables a considerably higher recognition performance on segmented object views.