# Feature Size

Focused mode tools analyze images based on the Feature Size parameter that you specify. The feature size, which is based in pixels, functions as a hint to the tool about the expected size of "meaningful" or "distinctive" features in the input images. The best method for selecting a feature size is to examine the input images as if you were a human inspector. Note the features in the image that you would use to characterize the image as good or bad, to identify a defect or problem, or to determine where something was and what it was.

For example, if you were attempting to classify pictures of airplanes based on the number of engines, the feature size would be based on the approximate size of an airplane engine.

During both training and runtime, the tool collects samples from the image that correspond to the pixels within a subregion of the image, as well as contextual information around that region. The contextual region is approximately five times the feature size. For more information, see Feature Sampling.

1 | Feature Size
2 | Sample Region
3 | Context Region

## Feature Size Optimization

Feature size strongly influences processing time (n2 ), and to a lesser extent, the time required to train a tool. A feature size of 100 is 100 times faster than a size of 10, while a feature size less than 15 usually does not yield good results. A larger feature size is much quicker to process, but larger feature sizes cannot "see" or "respond to" small features.

For both training and runtime, each tool collects image samples that completely cover the extent of the image.

During training, the tool samples the entire extent of the input image, and it oversamples regions that it determines contain more information.