Image feature associations via local semantic structure
Research in the field of object recognition suffers from two distinct weaknesses that limits its effectiveness in natural environments. The first is that this research tends to rely on labeled training images, or other forms of supervision, to learn object models and recognize these models in novel images, thus preventing the learning of objects that are not labeled by humans. The second is that such systems tend to assume that the goal is to recognize a single, dominant foreground object. This research implements a different method of object recognition that learns, with- out supervision, which ...
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