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Title: Sparse hierarchical models of vision
Author: Nath, Radhika
ISNI:       0000 0004 7960 165X
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2017
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In recent years, deep convolutional neural networks (CNNs), which are based on the hierarchical structure of the visual cortex has found remarkable accuracy rates in object classification. One of its drawbacks is the requirement of a large collection of labelled data for training. Therefore, unsupervised hierarchical networks are more suited for a more biologically plausible model. For a classification accuracy to be closer towards CNNs, the invariance and selectivity of the extracted features need to be improved. One of the standard methods for learning invariant features is to apply non-linearity functions to the data which has been implemented in both CNNs and HMAX models. Based on these principles, an extended form of the HMAX model is proposed which applies two different types of non-linear pooling operations. The extension is designed with the help of sparsity based algorithms such as Independent Subspace Analysis (ISA) and Topographic Independent Component Analysis(TICA). Aside from an improved classification accuracy compared to previous unsupervised hierarchical models, it also reduces the data dimensions within the layers.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available