Cortically-Inspired Overcomplete Feature Learning for Colour Images

No Thumbnail Available
File version
Author(s)
Cowley, Benjamin
Kneller, Adam
Thornton, John
Primary Supervisor
Other Supervisors
Editor(s)

Duc Nghia Pham, Seong-Bae Park

Date
2014
Size
File type(s)
Location

Gold Coast, Australia

License
Abstract

The Hierarchical Temporal Memory (HTM) framework is a deep learning system inspired by the functioning of the human neocortex. In this paper we investigate the feasibility of this framework by evaluating the performance of one component, the spatial pooler. Using a recently developed implementation, the augmented spatial pooler (ASP), as a single layer feature detector, we test its performance using a standard image classification pipeline. The main contributions of the paper are the implementation and evaluation of modifications to ASP that enable it to form overcomplete representations of the input and to form connections with multiple data channels. Our results show that these modifications significantly improve the utility of ASP, making its performance competitive with more traditional feature detectors such as sparse restricted Boltzmann machines and sparse auto-encoders.

Journal Title
Conference Title

13th Pacific Rim International Conference on Artificial Intelligence, Proceedings

Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Computer Vision

Persistent link to this record
Citation