Polynomial-based rotation invariant features

TL;DR

This paper introduces a polynomial-based method for generating large sets of rotation-invariant features in multiple dimensions, improving upon traditional harmonic approaches for shape comparison and recognition.

Contribution

It proposes a general approach to construct arbitrarily large sets of rotation invariants of polynomials, potentially providing complete invariants for shape analysis.

Findings

Constructs up to O(n^D) invariants for degree D in R^n.

Offers a method to obtain complete sets of rotation invariants.

Enhances shape similarity evaluation and recognition tasks.

Abstract

One of basic difficulties of machine learning is handling unknown rotations of objects, for example in image recognition. A related problem is evaluation of similarity of shapes, for example of two chemical molecules, for which direct approach requires costly pairwise rotation alignment and comparison. Rotation invariants are useful tools for such purposes, allowing to extract features describing shape up to rotation, which can be used for example to search for similar rotated patterns, or fast evaluation of similarity of shapes e.g. for virtual screening, or machine learning including features directly describing shape. A standard approach are rotationally invariant cylindrical or spherical harmonics, which can be seen as based on polynomials on sphere, however, they provide very few invariants - only one per degree of polynomial. There will be discussed a general approach to construct arbitrarily large sets of rotation invariants of polynomials, for degree $D$ in $\mathbb{R}^n$ up to $O(n^D)$ independent invariants instead of $O(D)$ offered by standard approaches, possibly also a complete set: providing not only necessary, but also sufficient condition for differing only by rotation (and reflectional symmetry).