Data mining within digital images

摘要

Previous papers have studied the relationship between a bit map digital image and a given object, called the search object. In particular, to signal that it is likely, or not likely, that the search object appears, at least partially, in the image. Edges in the search object and in the digital image are then represented as objects, in the object oriented programming sense. Each edge or segment of an edge is represented as a normalized Bezier cubic parameterized curve. The normalization process is intended to remove the effect of size in the edge or edge segment. If the edges match and their orientation is the same, then the system signals that the object is likely to appear in the image and the coordinates in the image of the object are returned. The functioning of the algorithm is not dependent on scaling, rotation, translation, or shading of the image. To begin the data mining process, a collection of search objects is generated. A database is constructed using a number of images and storing information concerning the combination of search objects that appear in each image, time and space relationships between the various search objects, along with identifying information about the image. This database would then be subjected to traditional data mining techniques in order to generate useful relationships within the data. These relationships could then be used to advantage in supplying information for defense, corporate, or law enforcement intelligence.