Fast Density Codes for Image Data

TL;DR

This paper introduces a simplified and significantly faster variant of the Density Codes method tailored for image data, enabling efficient comparison and shape analysis in large image databases.

Contribution

The paper presents a new, faster density coding technique specifically designed for image data, improving computational efficiency while maintaining key invariance properties.

Findings

Encoding speed increased by thousands of times

Maintains invariance to affine and non-affine transformations

Applicable to large image datasets efficiently

Abstract

Recently, a new method for encoding data sets in the form of "Density Codes" was proposed in the literature (Courrieu, 2006). This method allows to compare sets of points belonging to every multidimensional space, and to build shape spaces invariant to a wide variety of affine and non-affine transformations. However, this general method does not take advantage of the special properties of image data, resulting in a quite slow encoding process that makes this tool practically unusable for processing large image databases with conventional computers. This paper proposes a very simple variant of the density code method that directly works on the image function, which is thousands times faster than the original Parzen window based method, without loss of its useful properties.