Accuracy versus speed in context-based object detection

The visual detection and recognition of objects is facilitated by context. This paper studies two types of learning methods for realizing context-based object detection in paintings. The first method is called the gradient method; it learns to transform the spatial context into a gradient towards the object. The second method, the context-detection method, learns to detect image regions that are likely to contain objects. The accuracy and speed of both methods are evaluated on a face-detection task involving natural and painted faces in a wide variety of contexts. The experimental results show that the gradient method enhances accuracy at the cost of computational speed, whereas the context-detection method optimises speed at the cost of accuracy. The different results of both methods are argued to arise from the different ways in which the methods trade-off accuracy and speed. We conclude that both the gradient method and the context-detection method can be applied to reliable and fast object detection in paintings and that the choice for either method depends on the application and user constraints.