The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of 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 information that is available on some pixels and specifies constraints on those pixels belonging to the same or different clusters while 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.

翻译：由于仪器性能提升和成本降低，高光谱成像在食品样本检测中的应用日益广泛。食品工程师通常采用分类方法，利用高光谱图像对食品样本的类型和质量进行分类。为训练这些方法，需要对每幅训练图像中的每个像素进行标注。通常采用计算成本较低的基于阈值的方法对像素进行标注，并基于这些标签训练分类方法。然而，基于阈值的方法具有主观性，且无法泛化到不同条件下拍摄的不同食品的高光谱图像。本文提出一种基于共识约束的简约高斯混合模型（ccPGMM），采用基于模型的聚类方法对高光谱图像中的像素进行标注。ccPGMM利用部分像素的可用信息，在对图像中其余像素进行聚类的同时，对这些像素所属的相同或不同聚类施加约束。采用潜变量模型，通过少量潜在因子表示高维数据。为确保计算可行性，采用共识聚类方法：将数据划分为多个随机选择的变量子集，对每个数据子集施加约束聚类；随后整合所有数据子集的聚类结果，形成共识聚类解。将ccPGMM方法应用于模拟数据集和三种膨化谷物（玉米、大米、小麦）的真实高光谱图像。与当前其他先进方法相比，该方法展现出更优的聚类性能和计算效率。