Abstract：
:Shape primitives have long been proposed as components for object models in the visual system, and account for a considerable body of behavioral findings. While a large amount of effort has been devoted to the study of detection of these parts in the scenes, no research has been undertaken simulating the acquisition of these representations. We present a model which suggests how the shape primitives may be learned by experience in a self-organized fashion. This model offers the first successful unsupervised learning of shape primitives which are as complex as object parts and can serve as intermediate representations for various objects. The algorithm uses synthetic gray-level objects, each composed of several parts (primitives or else), and shape primitives emerge as a result of partial matches between several objects. Our algorithm does not use any a priori knowledge about any attributes of the patterns to be learned; and the recurrence of these visual patterns in various objects is the only basis for their emergence as new features.
journal_name
Vision Resjournal_title
Vision researchauthors
Shams L,von der Malsburg Cdoi
10.1016/s0042-6989(02)00130-xsubject
Has Abstractpub_date
2002-08-01 00:00:00pages
2105-22issue
17eissn
0042-6989issn
1878-5646pii
S004269890200130Xjournal_volume
42pub_type
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