用于高光谱图像聚类的共识约束简约高斯混合模型
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
AI总结：
针对高光谱图像像素标注中基于阈值方法的主观性与泛化性问题，提出共识约束简约高斯混合模型（ccPGMM），通过潜变量模型和共识聚类提升聚类性能与计算效率，在食品样本高光谱图像中验证有效。
摘要：
由于仪器性能提升和成本降低，利用高光谱成像研究食品样本的应用日益广泛。食品工程师通常采用分类方法，通过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，每张训练图像的每个像素都需要标注。通常，计算成本较低的基于阈值的方法被用于像素标注，分类方法则基于这些标注进行训练。然而，基于阈值的方法具有主观性，无法在不同条件下拍摄的不同食品的高光谱图像之间进行泛化。本文提出一种共识约束简约高斯混合模型（ccPGMM），用于高光谱图像的像素聚类标注。该模型利用部分像素的可用信息，对属于相同或不同聚类的像素施加约束，同时对图像其余像素进行聚类。模型采用潜变量模型，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，对每个子集应用约束聚类，然后整合所有子集的聚类结果得到共识聚类解决方案。ccPGMM方法在模拟数据集和三种膨化谷物（玉米、大米、小麦）的真实高光谱图像上进行了验证，与现有最先进方法相比，展现出更优的聚类性能和计算效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
