Recently, the field of computer vision has made significant advances by using deep learning mechanisms, such as Convolutional Neural Networks (CNN) for image analysis . In traditional approaches experts manually created features that represent information about an image (e.g., shapes, textures, colors). In contrast, when using a CNN for image analysis, the features are automatically created by the neural network during the learning phase. Since training a large CNN is a resource intensive task which also requires a large training set of labelled data, researchers had the idea to use the extracted features of a well-trained CNN, so called “off-the-shelf CNN features”, for different image analysis tasks. Results suggest that the features from CNN perform very well in most visual recognition tasks .