%0 Journal Article %1 Bergboer2007 %A Bergboer, Niek %A Postma, Eric %A van den Herik, Jaap %B Pattern Recognition in Cultural Heritage and Medical Applications %D 2007 %J Pattern Recognition Letters %K Computer Face Machine Object detection, learning vision, %N 6 %P 686--694 %T Accuracy versus speed in context-based object detection %U http://www.sciencedirect.com/science/article/B6V15-4KXDR0D-3/2/f7a7fbb9822da2f312adfb1e192da989 %V 28 %X The visual detection and recognition of objects is facilitated by context. This paper studies two types of learning methods for realizing context-based object detection in paintings. The first method is called the gradient method; it learns to transform the spatial context into a gradient towards the object. The second method, the context-detection method, learns to detect image regions that are likely to contain objects. The accuracy and speed of both methods are evaluated on a face-detection task involving natural and painted faces in a wide variety of contexts. The experimental results show that the gradient method enhances accuracy at the cost of computational speed, whereas the context-detection method optimises speed at the cost of accuracy. The different results of both methods are argued to arise from the different ways in which the methods trade-off accuracy and speed. We conclude that both the gradient method and the context-detection method can be applied to reliable and fast object detection in paintings and that the choice for either method depends on the application and user constraints.

@article{Bergboer2007, abstract = {The visual detection and recognition of objects is facilitated by context. This paper studies two types of learning methods for realizing context-based object detection in paintings. The first method is called the gradient method; it learns to transform the spatial context into a gradient towards the object. The second method, the context-detection method, learns to detect image regions that are likely to contain objects. The accuracy and speed of both methods are evaluated on a face-detection task involving natural and painted faces in a wide variety of contexts. The experimental results show that the gradient method enhances accuracy at the cost of computational speed, whereas the context-detection method optimises speed at the cost of accuracy. The different results of both methods are argued to arise from the different ways in which the methods trade-off accuracy and speed. We conclude that both the gradient method and the context-detection method can be applied to reliable and fast object detection in paintings and that the choice for either method depends on the application and user constraints.}, added-at = {2009-09-12T19:19:34.000+0200}, author = {Bergboer, Niek and Postma, Eric and van den Herik, Jaap}, biburl = {https://www.bibsonomy.org/bibtex/2d851804c57ce32c68c1b0555fb1237f1/mozaher}, booktitle = {Pattern Recognition in Cultural Heritage and Medical Applications}, file = {Accuracy versus speed.pdf:Bergboer2007.pdf:PDF}, interhash = {ce3e16272fd504f2127b307a868f1587}, intrahash = {d851804c57ce32c68c1b0555fb1237f1}, journal = {Pattern Recognition Letters}, keywords = {Computer Face Machine Object detection, learning vision,}, month = {April}, number = 6, owner = {Mozaher}, pages = {686--694}, timestamp = {2009-09-12T19:19:36.000+0200}, title = {Accuracy versus speed in context-based object detection}, url = {http://www.sciencedirect.com/science/article/B6V15-4KXDR0D-3/2/f7a7fbb9822da2f312adfb1e192da989}, volume = 28, year = 2007 }