Visual novelty detection with automatic scale selection

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Visual novelty detection with automatic scale selection (2007)

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by Hugo Vieira Neto A , Ulrich Nehmzow B

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@MISC{A07visualnovelty,
    author = {Hugo Vieira Neto A and Ulrich Nehmzow B},
    title = {Visual novelty detection with automatic scale selection},
    year = {2007}
}

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Abstract

This paper presents experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment from camera images. The experiments used two different attention mechanisms – saliency map and multi-scale Harris detector – and two different novelty detection mechanisms — the Grow-When-Required (GWR) neural network and an incremental Principal Component Analysis (PCA). For all mechanisms we compared fixed-scale image encoding with automatically scaled image patches. Results show that automatic scale selection provides a more efficient representation of the visual input space, but that performance is generally better using a fixed-scale image encoding. c ○ 2007 Elsevier B.V. All rights reserved.

Keyphrases

automatic scale selection visual novelty detection fixed-scale image multi-scale harris detector image patch efficient representation novel feature visual input space incremental principal component analysis elsevier b.v. right different novelty detection camera image neural network different attention mechanism saliency map autonomous inspection robot

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