12 February 2007 Feature category systems for 2nd order local image structure induced by natural image statistics and otherwise
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Abstract
We report progress on an approach (Geometric Texton Theory - GTT) that like Marr's 'primal sketch' aims to describe image structure in a way that emphasises its qualitative aspects. In both approaches, image description is by labelling points using a vocabulary of feature types, though compared to Marr we aim for a much larger feature vocabulary. We base GTT on the Gaussian derivative (DtG) model of V1 measurement. Marr's primal sketch was based on DtG filters of derivative order up to 2nd, for GTT we plan to extend to the physiologically plausible limit of 4th. This is how we will achieve a larger feature vocabulary (we estimate 30-150) than Marr's 'edge', 'line' and 'blob'. The central requirement of GTT then is for a procedure for determining the feature vocabulary that will scale up to 4th order. We have previously published feature category systems for 1-D 1st order, 1-D 2nd order, 2-D 1st order and 2-D pure 2nd order. In this paper we will present results of GTT as applied to 2-D mixed 1st + 2nd order features. We will review various approaches to defining the feature vocabulary, including ones based on (i) purely geometrical considerations, and (ii) natural image statistics.
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Lewis D. Griffin, Martin Lillholm, "Feature category systems for 2nd order local image structure induced by natural image statistics and otherwise", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649209 (12 February 2007); doi: 10.1117/12.714560; https://doi.org/10.1117/12.714560
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