Release Notes: This release adds bound constrained non-linear optimizers using the BFGS and L-BFGS methods. It also includes a new tool for learning a max-margin Mahalanobis distance metric as well as routines for easily computing Felzenszwalb's 31 channel HOG image representation.
Release Notes: This release focused on improving the speed and usability of dlib's structural support vector machine solver. Two new tutorial style example programs showing how to use the solver from either C++ or Python were included.
Release Notes: This release has primarily focused on improving the flexibility and ease of use of the object detection tools.
Release Notes: This release includes a large number of new minor features and usability improvements. It also includes a new machine learning tool for learning to rank objects. This is the dlib::svm_rank_trainer, an implementation of the well known SVM-Rank algorithm. Moreover, the implementation runs in O(n*log(n)) time and is therefore suitable for use with large training datasets.