A smart feature selection technique for object localization in ocular fundus images with the aid of color subspaces

We propose a technique for selecting effective features for object localization in ocular fundus images. The technique has made it possible to conduct smart feature analysis with the aid of color subspaces when solving a problem of selecting the areas of interest. The relevance of the problem is associated with enhancing the effectiveness of laser coagulation surgery. The proposed technique enables not only the informative features to be extracted in particular color spaces but also the most informative color subspace to be identified. The technique allows an effective feature for separating two particular classes to be identified at a definite size of the fragmentation block thanks to the use of various feature selection rules. The technique also makes it possible to find a universal feature using which two particular initial classes can be separated with a minimal clustering error in all color subspaces, also finding a color-specific in-formative feature enabling the majority of classes under study to be separated. The most informative color subspace was defined.