Measuring the Objectness of Image Windows
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34/11 (2012), pp. 2189-2202
Publication Year
2012
Authors
Bogdan Alexe, Thomas Deselaers, Vittorio Ferrari
BibTeX
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. These
include an innovative cue to measure the closed boundary characteristic. In
experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to
outperform a state-of-the-art saliency measure, and the combined objectness measure
to perform better than any cue alone. We also compare to interest point operators,
a HOG detector, and three recent works aiming at automatic object segmentation.
Finally, we present two applications of objectness. In the first, we sample a small
number windows according to their objectness probability and give an algorithm to
employ them as location priors for modern class-specific object detectors. As we
show experimentally, this greatly reduces the number of windows evaluated by the
expensive class-specific model. In the second application, we use objectness as a
complementary score in addition to the class-specific model, which leads to fewer
false positives. As shown in several recent papers, objectness can act as a
valuable focus of attention mechanism in many other applications operating on image
windows, including weakly supervised learning of object categories, unsupervised
pixelwise segmentation, and object tracking in video. Computing objectness is very
efficient and takes only about 4 sec. per image.
