I investigate properties of HMAX, a computational model of hierarchical processing in the primate visual cortex. High-level cortical neurons have been shown to respond highly to particular natural shapes, such as faces. HMAX models this property with a dictionary of natural shapes, called prototypes, that respond to the presence of those shapes. The resulting set of similarity measurements is an effective descriptor for classifying images. Curiously, prior work has shown that replacing the dictionary of natural shapes with entirely random prototypes has little impact on classification performance. This work explores that phenomenon by studying the performance of random prototypes on natural scenes, and by comparing their performance to that of sparse random projections of low-level image features.
|Commitee:||Liu, Feng, Walpole, Jonathan|
|School:||Portland State University|
|School Location:||United States -- Oregon|
|Source:||MAI 55/03M(E), Masters Abstracts International|
|Keywords:||Computational neuroscience, Computer vision, Machine learning, Random projection|
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