A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis

TL;DR

This paper introduces a hierarchical PCA-based texture model that reduces feature redundancy and effectively synthesizes natural textures with fewer dimensions than previous models.

Contribution

It proposes a novel hierarchical PCA approach to reduce the dimensionality of texture features, improving texture synthesis efficiency.

Findings

Effective texture description with fewer dimensions

Successful texture synthesis from contracted features

Reduces redundancy in texture feature representation

Abstract

Modeling of textures in natural images is an important task to make a microscopic model of natural images. Portilla and Simoncelli proposed a generative texture model, which is based on the mechanism of visual systems in brains, with a set of texture features and a feature matching. On the other hand, the texture features, used in Portillas' model, have redundancy between its components came from typical natural textures. In this paper, we propose a contracted texture model which provides a dimension reduction for the Portillas' feature. This model is based on a hierarchical principal components analysis using known group structure of the feature. In the experiment, we reveal effective dimensions to describe texture is fewer than the original description. Moreover, we also demonstrate how well the textures can be synthesized from the contracted texture representations.