New York, June 9 (IANS) Researchers at Disney Research Pittsburgh have developed a computer vision system that, much like humans, can continuously improve its ability to recognise objects by picking up hints while watching videos.
Recognising objects in images, though often easy for humans, remains a challenge for automated systems. Systems that learn to recognise objects using one set of images may have difficulty recognising those same objects in the real world, or under different sets of conditions, or domains.
Like most other object recognition systems, the Disney system builds a conceptual model of an object by using a learning algorithm to analyse number of example images of the object.
What’s different about the Disney system is that it then uses that model to identify objects, when it can, in videos. As it does, it sometimes is able to glean new information about such objects, enabling it to make its own model of the object more complex.
And that in turn enables the system to more readily recognise such objects in a wider variety of conditions.
“This process continues, potentially indefinitely, over the lifetime of the recognition system,” said Leonid Sigal, a senior research scientist at Disney Research.
“This is a learning system that is continuously evolving through unsupervised experience to build a more complete and complex model of the world,” Sigal added.
Sigal and colleagues would present their findings at the IEEE Conference on Computer Vision and Pattern Recognition, CVPR in Boston.