Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification

Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various implementations of DL algorithms have been released for the public use in the form of application programming interfaces (API). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft's Computer Vision, TensorFlow, Imagga, and IBM's Visual Recognition showed better performance than the other APIs. Furthermore, the new semantic similarity metric allowed deeper insights for comparison.