Image Classification Using Probabilistic Models that Reflect the Internal Structure of Mixels

Abstract

The purpose of this paper is to establish an image classification method which properly considers the spatial quantisation effect of digital imagery and its inevitable consequence, the presence of mixels. To achieve this goal, we propose two new probabilistic models, namely the area proportion distribution and mixel distribution. The former probabilistic model serves as the prior distribution of area proportions that reflect the internal structure of mixels, and Beta distribution is proposed as the general model of the area proportion distribution. On the other hand, the latter probabilistic model is a unique model both in concept and shape, and its uniqueness is the source of its effectiveness against an image histogram which can be represented by a set of trough and peak regions by means of the mixel distributions and pure pixel distributions, respectively. Moreover, the expected area proportion is proposed for computing the area proportions of mixels. Finally, experimental results on satellite image classification are analysed to validate the effectiveness of our proposed probabilistic models. By comparing the mixture density model with our proposed models to those without them, we conclude that, both in terms of quantitative and qualitative evaluation, our probabilistic models work effectively for images with the presence of mixels.