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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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

Collections

Department of Computer Engineering, Conference / Seminar