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AI Technology

New Object Recognition Algorithm Learns On the Fly 100

Zothecula writes "Scientists at Brigham Young University (BYU) have developed an algorithm that can accurately identify objects in images or videos and can learn to recognize new objects on its own. Although other object recognition systems exist, the Evolution-Constructed Features algorithm is notable in that it decides for itself what features of an object are significant for identifying the object and is able to learn new objects without human intervention."
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New Object Recognition Algorithm Learns On the Fly

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  • by barlevg ( 2111272 ) on Monday January 20, 2014 @03:49PM (#46016877)
    ...but I don't think an evolutionary algorithm approach to pattern recognition is anything new.
  • by fatgraham ( 307614 ) on Monday January 20, 2014 @04:28PM (#46017339) Homepage

    Does it work in real time? I can't find any more information than marketing buzz in the article (and the BYU article)...

    Is there a paper or anything with a bit more [technical] detail?

  • by jopet ( 538074 ) on Monday January 20, 2014 @05:56PM (#46018275) Journal

    Why should I get excited about something written by a journalist where there must be something writeen by scientists? Where is the PDF of the scientific paper to download?

  • by timeOday ( 582209 ) on Monday January 20, 2014 @05:56PM (#46018285)
    There is no way an image-based unsupervised algorithm can learn to recognize objects. "Shapes that frequently go together," yes. But what we consider "objects" is not an objective reality, it is a mental construct that is largely functionally-determined. It will never figure out all the different forms to which we ascribe the label "chair."

    Sitting on a street, a "bicycle" is an object because it is most like to be operated on as a unit. But to a bicycle mechanic, a bicycle is a collection of objects, such as a frame, a seat.. and so on because they need to decompose the "bicycle" construct to do their job. To somebody on an assembly line putting together bicycle seats, a seat is (at least initially) several different objects.

    So, truly unsupervised algorithms cannot do useful recognition - that is, classify objects the same way people do. (A robot that could experiment with its environment and learn to use "objects" could come closer).

e-credibility: the non-guaranteeable likelihood that the electronic data you're seeing is genuine rather than somebody's made-up crap. - Karl Lehenbauer