A new image classification algorithm based on additive groves.

摘要

Image analysis and classification have become a very active research topic in recent decades. The practical applications where classification of images is of importance range from technology to geographical, civil, military, and biological sciences. This article proposes an efficient image classification algorithm based on a recently developed regression method. Groves of regression trees are used with image feature sampling to learn patterns from images that are later used to define a probabilistic framework for classification purposes. This new method can generalize well with small number of training subwindows. By comparing several publicly available datasets, this paper shows how the new method outperforms several state of the art techniques.