Spatial modelling for mixed-state observations

TL;DR

This paper introduces spatial models tailored for mixed-state observations, combining discrete symbolic data and continuous measurements, with applications demonstrated in motion analysis from video sequences.

Contribution

It develops multi-parameter auto-models with local conditionals in a mixed state exponential family, addressing a gap in modeling mixed discrete and continuous data.

Findings

Effective modeling of motion measurements from video sequences.

Demonstrated suitability of mixed state auto-models for real-world data.

Potential for improved analysis in fields like pluviometry and image sequences.

Abstract

In several application fields like daily pluviometry data modelling, or motion analysis from image sequences, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". This paper introduces spatial models suited for the analysis of these kinds of data. We consider multi-parameter auto-models whose local conditional distributions belong to a mixed state exponential family. Specific examples with exponential distributions are detailed, and we present some experimental results for modelling motion measurements from video sequences.