A procedure for automatic object-based classification

A human observer can easily categorize an image into classes of interest but it is generally difficult to reproduce the same result using a computer. The emerging object-based methodology for image classification appears to be a better way to mimic the human thought process. Unlike pixel-based techniques which only use the layer pixel values, the object-based techniques can also use shape and context information of a scene texture. These extra degrees of freedom provided by the objects will aid the identification (or classification) of visible textures. However, the concept of image-objects brings with it a large number of object features and thus a lot of information is associated with the objects. In this article, we present a procedure for object-based classification which effectively utilizes the huge information associated with the objects and automatically generates classification rules. The solution of automation depends on how we solve the problem of identifying the features that characterize the classes of interest and then finding the final distribution of the classes in the identified feature space. We try to illustrate the procedure applied for a two-class case and then suggest possible ways to extend the method for multiple classes.