Object Recognition Using Stochastic Optimization

Shimon Ullman, Assaf Zeira

Abstract: We describe an approach to object recognition in which the image-to-model match is based on stochastic optimization. During the recognition process, an internal model is matched with a novel object view. To compensate for changes in viewing conditions (such as illumination, viewing direction), the model is controlled by a number of parameters. The matching is obtained by seeking a setting of the parameters that minimizes the discrepancy between the image and the model. The search is performed in our examples in a six-dimensional space with multiple local minima. We developed an efficient minimization method based on the stochastic optimization approach (Mockus 1989). The search is bi-directional (applied to both the model and the image) and avoids the difficult problem of establishing image-to-model correspondence. It proceeds by evolving a population of candidate solutions using simple generation rules, based on the autocorrelation of the search space. We describe the method, its application to objects in several domains (cars, faces, printed symbols), and experimental comparisons with alternative methods, such as simulated annealing.