  The use of hyperspectral imaging to investigate food samples has grown due to
the improved performance and lower cost of spectroscopy instrumentation. Food
engineers use hyperspectral images to classify the type and quality of a food
sample, typically using classification methods. In order to train these
methods, every pixel in each training image needs to be labelled. Typically,
computationally cheap threshold-based approaches are used to label the pixels,
and classification methods are trained based on those labels. However,
threshold-based approaches are subjective and cannot be generalized across
hyperspectral images taken in different conditions and of different foods. Here
a consensus-constrained parsimonious Gaussian mixture model (ccPGMM) is
proposed to label pixels in hyperspectral images using a model-based clustering
approach. The ccPGMM utilizes available information on the labels of a small
number of pixels and the relationship between those pixels and neighbouring
pixels as constraints when clustering the rest of the pixels in the image. A
latent variable model is used to represent the high-dimensional data in terms
of a small number of underlying latent factors. To ensure computational
feasibility, a consensus clustering approach is employed, where the data are
divided into multiple randomly selected subsets of variables and constrained
clustering is applied to each data subset; the clustering results are then
consolidated across all data subsets to provide a consensus clustering
solution. The ccPGMM approach is applied to simulated datasets and real
hyperspectral images of three types of puffed cereal, corn, rice, and wheat.
Improved clustering performance and computational efficiency are demonstrated
when compared to other current state-of-the-art approaches.
