Snakes in trees: an explainable artificial intelligence approach for automatic object detection and recognition

Nowadays, the development of smart cities boosts the development of innovative IT technologies based on Artificial Intelligence (AI), such as intelligent agents (IA), which themselves use new algorithms, complex software, and advanced systems. However, due to their expanding number and range of applications as well as their growing autonomy, there is an increased expectation for these intelligent technologies to involve explainable algorithms, dependable software, trustworthy systems, transparent agents, etc. Hence, in this paper, we present a new explainable algorithm which uses snakes within trees to automatically detect and recognize objects. The proposed method involves the recursive computation of snakes (aka parametric active contours), leading to multi-layered snakes where the first layer corresponds to the main object of interest, while the next-layer snakes delineate the different sub-parts of this foreground. Visual features are extracted from the regions segmented by these snakes and are mapped into semantic concepts. Based on these attributes, decision trees are induced, resulting in effective semantic labeling of the objects and the automatic annotation of the scene. Our computer-vision approach shows excellent computational performance on real-world standard database, in context of smart cities.