Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Frequently Asked Questions
Frequently Asked Questions
Communities & Collections
Communities & Collections
A Framework for Machine Vision based on Neuro-Mimetic Front End Processing and Clustering
Date
2014-10-03
Author
Akbaş, Emre
WADHWA, Aseem
ECKSTEIN, Miguel
MADHOW, Upamanyu
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
5
views
0
downloads
Convolutional deep neural nets have emerged as a highly effective approach for machine vision, but there are a number of open issues regarding training (e.g., a large number of model parameters to be learned, and a number of manually tuned algorithm parameters) and interpretation (e.g., geometric interpretations of neurons at various levels of the hierarchy). In this paper, our goal is to explore alternative convolutional architectures which are easier to interpret and simpler to implement. In particular, we investigate a framework that combines a front end based on the known neuroscientific findings about the visual pathway, together with unsupervised feature extraction based on clustering. Supervised classification, using a generic radial basis function (RBF) support vector machine (SVM), is applied at the end. We obtain competitive classification results on standard image databases, beating the state of the art for NORB (uniform-normalized) and approaching it for MNIST.
Subject Keywords
Receptive-Fields
,
Ganglion-Cells
,
Object Recognition
,
Spatial Structure
URI
https://hdl.handle.net/11511/39770
DOI
https://doi.org/10.1109/allerton.2014.7028471
Collections
Department of Computer Engineering, Conference / Seminar