A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming
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A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming Mengjie Zhang School of Mathematical and Computing Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand Email: [email protected]
Victor B. Ciesielski School of Computer Science and Information Technology, RMIT University, GPO Box 2476v Melbourne, 3001 Victoria, Australia Email: [email protected]
Peter Andreae School of Mathematical and Computing Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand Email: [email protected] Received 30 June 2002 and in revised form 7 March 2003 This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems. Keywords and phrases: machine learning, neural networks, genetic algorithms, object recognition, target detection, computer vision.
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INTRODUCTION
As more and more images are captured in electronic form, the need for programs which can find objects of interest in a database of images is increasing. For example, it may be necessary to find all tumors in a database of x-ray images, all cyclones in a database of satellite images, or a particular face in a database of photographs. The common characteristic of such problems can be phrased as “given subimage1 , subimage2 , . . . , subimagen which are examples of the objects of interest, find all images which contain this object and its location(s).” Figure 10 shows examples of problems of this kind. In the problem illustrated by Figure 10b, we want to find centers of all of the Australian 5-cent and 20-cent coins and determine whether the head or the tail side is up. Examples of other problems of this kind include target detection problems [1, 2, 3], where the task is to find, say, all tanks, trucks, or helicopters in an image. Unlike most of the cur-
rent work in the object recognition area, where the task is to detect only objects of one class [1, 4, 5], our objective is to detect objects from a number of classes. Domain independence means that the same method will work unchanged on any problem, or at least on some range of problems. This is very difficult to achieve at the current state of the art in co
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