Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends

This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from similar to 2000s to just 0.1s, respectively. These results demonstrate the feasibility of using these two simple multivariate statistical techniques for image classification.

Automated summary

This article describes simple methods, such as principal component analysis (PCA) and hierarchical clustering of principal components (HCPC), to group images. The results of applying PCA and HCPC to expanded and low expanded extrudates showed consistently similar results with the same distribution and groups. Additionally, the optimization alternatives of image size reduction and grayscale conversion reduced the allocated RAM and processing time significantly.