Kernelized spatial-contextual image classification

A computer-readable medium comprising instructions executable by a computing device to categorize a plurality of unlabeled images, the instructions being executable to perform a method comprising:

• generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node;
• generating a second spatial-contextual model to represent a second image, the second spatial-contextual model having a plurality of interconnected nodes arranged in the first pattern of connections;
• training a Universal Reference Model from a plurality of referential images;
• adapting the first spatial-contextual model and the second spatial-contextual model to the Universal Reference Model;
• determining a relationship of adjacent nodes that each node is connected with in the first pattern of connections;
• and
• calculating a distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on the relationship of adjacent connected nodes.

A system to categorize a plurality of unlabeled images, the system with an input, a memory, and a processor in communication with the input and the memory, the system comprising:

• a model generation module configured to generate a first dependency tree hidden Markov model to represent a first image, the model generation module further configured to generate a second dependency tree hidden Markov model to represent a second image;
• a distance module in communication with the model generation module, the distance module configured to calculate an image distance between the first dependency tree hidden Markov model and the second dependency tree hidden Markov model;
• and
• a categorization module to receive the image distance and to generate a kernelized spatial-contextual model using the image distance.