%0 Journal Article %1 Bergboer2006 %A Bergboer, N.H. %A Postma, E.O. %A van den Herik, H.J. %D 2006 %J Image and Vision Computing %K 42.30.T, Computer Machine Object learning, recognition vision, %N 9 %P 987--1000 %T Context-based object detection in still images %U http://www.sciencedirect.com/science/article/B6V09-4JRKVGS-N/2/aefa8f7a9dbdc6a9824811fdbcc5cca7 %V 24 %X We present a novel dual-stage object-detection method. In the first stage, an object detector based on appropriate visual features is used to find object candidates. In the second stage, the object candidates are assigned a confidence value based on local-contextual information. Our context-based method is called COBA, for COntext BAsed object detection. At a given detection rate COBA is able to lower the false-detection rate. Experiments in which frontal human faces are to be detected show that the number of false positives is lowered by a factor 8.7 at a detection rate of 80% when compared to the current high-performance object detectors. Moreover, COBA is capable of flexibly using other new object-detection algorithms as `plug-ins' in the second stage. Hence, object detection can be straightforwardly improved by our method a soon as new insights emerge and are available in algorithmic form.

@article{Bergboer2006, abstract = {We present a novel dual-stage object-detection method. In the first stage, an object detector based on appropriate visual features is used to find object candidates. In the second stage, the object candidates are assigned a confidence value based on local-contextual information. Our context-based method is called COBA, for COntext BAsed object detection. At a given detection rate COBA is able to lower the false-detection rate. Experiments in which frontal human faces are to be detected show that the number of false positives is lowered by a factor 8.7 at a detection rate of 80% when compared to the current high-performance object detectors. Moreover, COBA is capable of flexibly using other new object-detection algorithms as `plug-ins' in the second stage. Hence, object detection can be straightforwardly improved by our method a soon as new insights emerge and are available in algorithmic form.}, added-at = {2009-09-12T19:19:34.000+0200}, author = {Bergboer, N.H. and Postma, E.O. and van den Herik, H.J.}, biburl = {https://www.bibsonomy.org/bibtex/25f67cc537d78b43f48fc25f4af6317c5/mozaher}, file = {contextbased.pdf:Bergboer2006.pdf:PDF}, interhash = {84ce44874fe068a3c21451be320385ae}, intrahash = {5f67cc537d78b43f48fc25f4af6317c5}, journal = {Image and Vision Computing}, keywords = {42.30.T, Computer Machine Object learning, recognition vision,}, month = Sep, number = 9, owner = {Mozaher}, pages = {987--1000}, timestamp = {2009-09-12T19:19:36.000+0200}, title = {Context-based object detection in still images}, url = {http://www.sciencedirect.com/science/article/B6V09-4JRKVGS-N/2/aefa8f7a9dbdc6a9824811fdbcc5cca7}, volume = 24, year = 2006 }