Towards generic image classification: an extensive empirical study

This paper considers the general problem of image classification
without using any prior knowledge about image classes. We study
variants of a method based on supervised learning whose common steps
are the extraction of random subwindows described by raw pixel intensity values
and the use of ensemble of extremely randomized trees to directly
classify images or to learn image features. The influence of method
parameters and variants is thoroughly evaluated so as to provide baselines and
guidelines for future studies. Detailed results are provided on 80
publicly available datasets that depict very diverse types of images
(more than 3800 image classes and over 1.5 million images).