Combination of features through weighted ensembles for image classification

Image classification is a multi-class problem that is usually tackled with ensembles of binary classifiers. Furthermore, one of the most important challenges in this field is to find a set of highly discriminative image features for reaching a good performance in image classification. In this work we propose to use weighted ensembles as a method for feature combination. First, a set of binary classifiers are trained with a set of features and then, the scores are weighted with distances obtained from another set of feature vectors. We present two different approaches to weight the score vector: (1) directly multiplying each score by the weights and (2) fusing the scores values and the distances through a Neural Network. The experiments have shown that the proposed methodology improves classification accuracy of simple ensembles and even more it obtains similar classification accuracy than state-of-the-art methods, but using much less parameters.