Self-Organizing Maps
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving object detection with boosted histograms
Image and Vision Computing
Use of image regions in context-adaptive image classification
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
A variational statistical framework for object detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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In this paper we consider the interaction between different semantic levels in still image scene classification and object detection problems. We present a method where a neural method is used to produce a tentative higher-level semantic scene representation from low-level statistical visual features in a bottom-up fashion. This emergent representation is then used to refine the lower-level object detection results. We evaluate the proposed method with data from Pascal VOC Challenge 2006 image classification and object detection competition. The proposed techniques for exploiting global classification results are found to significantly improve the accuracy of local object detection.