Learning Composite Operators For Object Detection
Bir Bhanu, Yingqiang Lin · 2002
In this paper, we learn to discover composite operators and features that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in images. Our approach is based on genetic programming (GP). The motivation for using GP is that there are a great many ways of combining these primitive operations and the human expert, limited by experience, knowledge and time, can only try a very small number of conventional ways of combination. Genetic programming, on the other hand, attempts many unconventional ways of combination that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. Our experimental results show that GP can find good composite operators, that consist of primitive operators designed in this paper, to effectively extract the regions of interest in images and the learned composite operators can be applied to extract ROIs in other similar images.