Multivariate feature extraction from textural images of bread

In order to compute the classical texture measures there is often a need to perform extensive calculations on the images and do a preprocessing in a specialised manner. Some of these texture measures are constructed to estimate specific information. Other texture measures seem to be more global in nature. The techniques presented in this paper define algorithms applied on the raw image without extensive preprocessing. We want to show that mathematical transformations of images on a vectorised form will easily enable the use of multivariate techniques and possibly model several features hidden in the images at the same time. In this paper we will compare five different methods of extracting features from textural images in food by multivariate modelling of the sensory porosity of wheat baguettes. The sample images are recorded from factorial designed baking experiments on wheat baguettes. The multivariate feature extraction methods to be treated are the angle measure technique (AMT), the singular value decomposition (SVD), the autocorrelation and autocovariance functions (ACF) and the so-called size and distance distribution (SDD) method. The methods will be tested on equal basis and the modelling of sensory porosity from extracted features is done using principal component regression (PCR) and partial least square regression (PLS). The difference between the behaviour of the methods will be discussed. The results show that all the methods are suited to extract sensory porosity but the AMT method prove to be the best in this case.