Image Classification with Approximately Biologically Realistic Elements
Document Type
Oral Presentation
Campus where you would like to present
SURC 137B
Start Date
21-5-2015
End Date
21-5-2015
Keywords
Machine Learning, Computational Intelligence, Artificial Spiking, Neural Network
Abstract
Image classification is a well studied problem, with applications such as face recognition and natural image classification. Here, image classification is done using a neural network of spiking neurons in a feedforward heirarchy that resembles certain structures of the visual cortex. Image encoding is done first through edge detection on the image, after which an HMAX model is used to bring about degrees of scale, position, rotation, and contrast-reverse invariance. Then, a single spiking neural network layer is trained to classify the encoded image using supervised learning. Results are shown for classification of single digit handwritten numbers.
Recommended Citation
Abdul-Wahid, Sami, "Image Classification with Approximately Biologically Realistic Elements" (2015). Symposium Of University Research and Creative Expression (SOURCE). 51.
https://digitalcommons.cwu.edu/source/2015/oralpresentations/51
Department/Program
Computational Science
Additional Mentoring Department
Computer Science
Image Classification with Approximately Biologically Realistic Elements
SURC 137B
Image classification is a well studied problem, with applications such as face recognition and natural image classification. Here, image classification is done using a neural network of spiking neurons in a feedforward heirarchy that resembles certain structures of the visual cortex. Image encoding is done first through edge detection on the image, after which an HMAX model is used to bring about degrees of scale, position, rotation, and contrast-reverse invariance. Then, a single spiking neural network layer is trained to classify the encoded image using supervised learning. Results are shown for classification of single digit handwritten numbers.
Faculty Mentor(s)
Razvan Andonie