Unsupervised statistical learning in vision : computational principles , biological evidence ∗
Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey a range of neurobiological findings and behavioral data consistent with it, and conclude by mentioning some of the more challenging directions for future research. 1 Why computer vision should care about unsupervised learning As the goals of computer vision grow more ambitious, the importance of learning becomes more difficult to deny: nobody wants to have to enter object and scene representations into his or her system by hand. But why should we insist that such learning be, in the first instance, unsupervised? Because we should not trust our analytical intuitions about the ontology of visual objects.Although the increasing availability of annotated image databases encourages the development of highly sophisticated supervised learning methods that combine linguistic and visual information (Duygulu et al., 2002), the success of such methods is limited by the poverty of the annotations, usually lexical labels. Indeed, a label (such as{cat, forest, grass, tiger }, shown in the work just cited) attached to a picture is both ontologically deficient in that it leaves out a host of possible complementary or alternative labels (Akins, 1996; Smith, 2001), and descriptively deficient in that it falls far short of providing a listener with a clear notion of the scene depicted (Kitcher and Varzi, 2000; Edelman, 2002). Thus, language conceived as a source of supervision for visual learning introduces biases that at best may be irrelevant, and at worst pernicious for any attempt to understand visual perception of objects and scenes. It would be prudent therefore to distrust ∗This is an extended abstract of a manuscript in preparation. Address correspondence to the first author at se37@cornell.edu . †On leave from the School of Computer Science, Tel-Aviv University, Tel Aviv 69678, Israel.