publication . Conference object . Other literature type . 2010

What is an object?

Alexe, Bogdan; Deselaers, Thomas; Ferrari, Vittorio;
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  • Published: 01 Jun 2010
  • Publisher: IEEE
Abstract
We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. This includes an innovative cue measuring the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-ar...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Object detection, Salience (neuroscience), Prior probability, Pixel, Image segmentation, Detector, Computer science, Bayesian probability, Pattern recognition, Artificial intelligence, business.industry, business, Computer vision
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