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Learning image components for object recognition

Spratling, Michael W (2006) Learning image components for object recognition. [Journal (Paginated)]

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Abstract

In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Non-negative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix factorisation algorithms. It is found that such algorithms need to impose additional constraints on the sparseness of the factors in order to successfully deal with occlusion. However, these constraints can themselves result in these algorithms failing to identify image components under certain conditions. In contrast, a recognition model (a competitive learning neural network algorithm) reliably and accurately learns representations of elementary image features without such constraints.

Item Type:Journal (Paginated)
Keywords:non-negative matrix factorisation; competitive learning; dendritic inhibition; object recognition
Subjects:Computer Science > Machine Vision
Computer Science > Machine Learning
Computer Science > Neural Nets
ID Code:4886
Deposited By:Spratling, Dr Michael
Deposited On:25 May 2006
Last Modified:11 Mar 2011 08:56

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