Gegenbauer-Based Image Descriptors for Visual Scene Recognition

Abstract

Visual scene recognition is an important problem in artificial intelligence with applications in areas such as autonomous vehicles, visually impaired people assistance, augmented reality, and many other pattern recognition areas. Visual scene recognition has been tackled in recent years by means of image descriptors such as the popular Speeded-Up Robust Features (SURF) algorithm. The problem consists in analyzing the scenes in order to produce a compact representation based on a set of so called regions of interest (ROIs) and then finding the largest number of matches among a dataset of reference images that include non-affine transformations of the scenes. In this paper, a new form of descriptors based on moment invariants from Gegenbauer orthogonal polynomials is presented. Their computation is efficient and the produced feature vector is compact, containing only a couple dozens of values. Our proposal is compared against SURF by means of the recognition rate computed on a set of two hundred scenes containing challenging conditions. The experimental results show no statistically significant difference between the performances of the descriptors.