Using context with statistical relational models: object recognition from observing user activity in home environment

Object recognition from images in a home environment is challenging since the object usually has low resolution in the image and the scene is usually cluttered. However, many objects have specific functions to the user and the interactions between the user and the object provides useful contextual information to recognize the object. In this paper, we use Markov logic network (MLN) to model such context information as relationship between the objects and user activities. We demonstrate that Markov logic network provides a flexible way in the syntax of first-order logic to incorporate relational context information. It is also a probabilistic graphical model which handles uncertainty in the knowledge base, observations and decisions. In our experiment, objects in the living room and kitchen in a home are recognized based on only user's activity. The user's activity is analyzed from images of cameras installed in the home. Relationship between user activity and objects is defined in a knowledge base with MLN. Experiments show that objects in the home can be recognized irrespective of their position, size and appearance in the image.