A Order based Content-Based 3D Model Retrieval proposal

Abstract

In computer graphics, Content-Based Retrieval (CBR) is a database system that consists of receiving an object as input and returning a list of similar objects. Although originally conceived for images, CBR systems can work with objects of another nature such as sounds, and three-dimensional models , represented as geometrical meshes, transporting CBR systems to contexts other than images To assess the proximity between the meshes, some techniques consist of performing mathematical transformations of the meshes into feature vectors and calculating the distance relationship between these vectors. In this work, we built a prototype of a 3D CBR system that uses an order relation, instead of a distance function, as a similarity function between the objects. Furthermore , we propose the use of an order in R n that we call Extended Lexicographical Order (ELO), which seeks to take into account all the information present in the vectors to be compared. We compared our prototype not only with the traditional distance functions but also with more classical R n order relations such as lexicographical and revlex. We also use two types of descrip-tors, a spectral descriptor based on compressed sensing theory and a harmonic spherical-based descriptor. In all cases, our prototype performed better when compared to traditional techniques.