How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?

In this work, we propose an unorthodox approach for describing a given image. Each image is represented by a feature vector whose elements are the scores assigned to object classes by deep convolutional neural networks that were not related to those that built the given image classification problem. The deep neural networks are trained using 1000 classes; therefore, each image is described by 1000 scores, which are fed to a support vector machine. The proposed approach could be considered a transfer learning method, where, instead of repurposing the learned features to a second classification problem, we use the scores obtained by trained convolutional neural networks. Methods based on state of the art handcrafted descriptors, and the novel approach presented here are compared, together with selected ensembles of such methods. The fusion between a standard approach and the new unorthodox method boosts the performance of the standard approach. The Wilcoxon signed rank test is used to compare the different methods. The novel method is applied to 21 different datasets to demonstrate its generality. The MATLAB source code to replicate our experiments will be available at(https://www.dei.unipd.it/node/2357 +Pattern Recognition and Ensemble Classifiers). (C) 2016 Elsevier B.V. All rights reserved.