Portland State University. Department of Computer Science
Date of Publication
Master of Science (M.S.) in Computer Science
Computer vision, Machine learning, Neural networks (Computer science)
1 online resource (x, 67 pages)
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.
Stewart, Kendall Lee, "The Performance of Random Prototypes in Hierarchical Models of Vision" (2015). Dissertations and Theses. Paper 2631.