Version 0.6.0b of Moron
Release Notes: This is a complete rewrite in the R language for statistical computing. Java and matlab are no longer required. Images are now described by a mix of local and global image properties with some spatial geometric information included. Random Forests are used as the classification engine. The current image categories attempted to be recognized are "nonpron", "pron", "latex/fetish", "japanese cg", "manga", and "bwpron". Detectors for other classes can now be easily built by supplying new training images in separate directories and running a few functions.
Other releases
Release Notes: Input image features are now extracted from rectangular, fixed-location regions in TSL colorspace, additional global image features are calculated by pooling stats from local regions, a new default training dataset is provided, DCT transform is fixed, and a few convenience functions are added for tweaking the dataset and observing the learning rate.
Release Notes: This version adds recognition of close-up and index images, and relies more heavily on global image properties. New feature extractors include Dominant Color features, Color Layout descriptor, ad-hoc Fourier statistics, and local 1st and 2nd order derivative statistics. General DCT and 2D DCT transforms were added. In addition, miscellaneous small fixes, features, and heuristics were implemented.
Release Notes: This is a complete rewrite in the R language for statistical computing. Java and matlab are no longer required. Images are now described by a mix of local and global image properties with some spatial geometric information included. Random Forests are used as the classification engine. The current image categories attempted to be recognized are "nonpron", "pron", "latex/fetish", "japanese cg", "manga", and "bwpron". Detectors for other classes can now be easily built by supplying new training images in separate directories and running a few functions.
Release Notes: This version adds LogitBoosted REPTrees and a Support Vector Machine as user-selectable classifier options. It now morphologically smoothes the visualized predictions, predicts a new class of 'black-and-white content', and uses a more carefully chosen training set for the 'drawn' class.
Release Notes: Images are now evaluated in multiple scales. It attempts to detect blond and brunette hair in addition to the classes present in 0.5.0. The feature extraction routines were tweaked and more training data added. This version requires Matlab, gcc, and Java.
