Assessing the Feasibility of a Spatio-Temporal Approach for Recognizing Microorganisms in Image Sequences

Data classification is one of the main challenges in data mining andknowledge discovery. It is widely explored in multiple applications,such as identifying microorganisms by analyzing images of culturesgrown in Petri dishes. This work proposes to chronologically organizeimages of bacteria on solid media, captured at equidistant intervalsover several days, treating them as video frames to improve therecognition of these microorganisms. To this end, we develop an approachthat chains classification models, integrating spatial featuresfrom pre-trained convolutional neural networks with temporal informationpropagated through target meta-features, i.e., classifieroutputs.We experimentally compared our proposal with two intentionallydesigned baseline methods using a dataset with 240 images,48 per class, and considering the macro-averaged F1 score. Resultsdemonstrate that addressing chronological relations enhances theidentification models’ performance, even though baseline strategiesmay benefit from fewer examples.