Distribution-based invariant feature construction using genetic programming for edge detection

In edge detection, constructing features with rich responses on different types of edges is a challenging problem. Genetic programming (GP) has been previously employed to construct features. Normally, the values of the features constructed by GP are calculated from raw observations. Some existing work has considered the distributions of the raw observations, but these features only poorly indicate class label probabilities. To construct features with rich responses on different types of edges, the distributions of the observations from GP programs are investigated in this study. The values of the constructed features are obtained from estimated distributions, rather than directly using the observations. These features themselves indicate probabilities for the target labels. Basic rotation-invariant features from gradients, image quality, and local histograms are used to construct new composite features. The results show that the invariant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance. In terms of the quantitative and qualitative evaluations, features constructed by GP with distribution estimation are better than the combinations from a Bayesian model and a linear support vector machine approach.