Assessing semantic information in convolutional neural network representations of images via image annotation

Image annotation, or prediction of multiple tags for an image, is a challenging task. Most current algorithms are based on large sets of handcrafted features. Deep convolutional neural networks have recently outperformed humans in image classification, and these networks can be used to extract features highly predictive of an image's tags. In this study, we analyze semantic information in features derived from two pre-trained deep network classifiers by evaluating their performance in nearest neighbor-based approaches to tag prediction. We generally exceed performance of the manual features when using the deep features. We also find complementary information in the manual and deep features when used in combination for image annotation.