Abstract: A learning model generates a plurality of prototype vectors and generates an integrated similarity vector that indicates similarity between an input image and each prototype for a plurality of prototypes in accordance with similarity between one prototype vector and each pixel vector in a feature map acquired from an CNN. An image processing apparatus obtains prototype belongingness (distributed prototype belongingness) for each image by distributing prototype belongingness of a belonged prototype of each class to each of two or more images that belong to one class. Then, the learning model is subjected to machine learning in accordance with the distributed prototype belongingness of each prototype vector for each image so that each prototype vector is brought closer to any pixel vector in the feature map corresponding to each image.