Image segmentation

A computer-implemented method of training image pixel classifiers, the method comprising:

• receiving an image formed of a plurality of pixels;
• receiving labelling data for labelling at least some of the pixels according to a hierarchical classification scheme, in which each of those pixels is labelled as belonging to one of a set p of parent classes Ai, each of which is associated with a subset q of child classes Bj, and each of those pixels is also labelled as belonging to one of the subset of child classes associated with the parent class to which it belongs, wherein each of the parent classes Ai corresponds to a category of visible structure, and each of the subset of child classes Bj associated with it corresponds to a different type of visible structure within that category;
• and
• using data of the pixels and the labelling data to train a parent level classifier to classify image pixels in relation to the set p of parent classes, and at least one child level classifier to classify image pixels in relation to the subset q of child classes associated with one of the parent classes;
• wherein the child level classifier is trained to determine an absolute child classification probability Pr(Px,y∈Bj) of that pixel belonging to a child class Bj of that subset of child classes, and the parent level classifier is trained to determine a parent classification probability Pr(Px,y∈Ai) of that pixel belonging to the parent class Ai with which that subset of child classes is associated;
• and
• wherein the child level classifier is trained to determine for that pixel a conditional child classification probability Pr(Px,y∈Bj|Px,y∈Ai) of the pixel Px,y belonging to that child class Bj, which is determined independently of the parent level classifier and the parent classification probability Pr(Px,y∈Ai), on an assumption that the pixel Px,y belongs to the parent class Ai;
• wherein the child level classifier is trained to determine the child classification probability Pr(Px,y∈Bj) by multiplying the parent classification probability Pr(Px,y∈Ai), with the conditional classification probability Pr(Px,y∈Bj|Px,y∈Ai) according to:
• Pr(Px,y∈Bj)=Pr(Px,y∈Bj|Px,y∈Ai)×Pr(Px,y∈Ai).

A computer program comprising computer readable instructions stored on a non-transitory storage medium and configured, when executed on one or more processors, to cause the one or more processors to implement:

• an input layer configured to receive an image formed of a plurality of pixels, and
• a plurality of classifiers configured to classify the pixels according to a hierarchical classification scheme, in which at least some of those pixels are classified by a parent level classifier of the plurality of classifiers in relation to a set of parent classes, each of which is associated with a subset of child classes, and each of those pixels is also classified by at least one child level classifier of the plurality of classifiers in relation to one of the subsets of child classes, wherein each of the parent classes corresponds to a category of visible structure, and each of the subset of child classes associated with it corresponds to a different type of visible structure within that category;
• wherein the child level classifier is configured to determine an absolute child classification probability Pr(Px,y∈Bj) of that pixel belonging to a child class Bj of that subset of child classes, and the parent level classifier is configured to determine a parent classification probability Pr(Px,y∈Ai) of that pixel belonging to the parent class Ai with which that subset of child classes is associated;
• wherein the child level classifier is configured to determine for that pixel a conditional child classification probability Pr(Px,y∈Bj|Px,y∈Ai) of the pixel Px,y belonging to that child class Bj, which is determined independently of the parent level classifier and the parent classification probability Pr(Px,y∈Ai), on the assumption that the pixel Px,y belongs to the parent class Ai;
• wherein the child level classifier is configured to determine the child classification probability Pr(Px,y∈Bj) by multiplying the parent classification probability Pr(Px,y∈Ai), with the conditional classification probability Pr(Px,y∈Bj|Px,y∈Ai) according to:
• Pr(Px,y∈Bj)=Pr(Px,y∈Bj|Px,y∈Ai)×Pr(Px,y∈Ai).