Automatically Annotating the MIR Flickr Dataset: Experimental Protocols, Openly Available Data and Semantic Spaces

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Description/Abstract

The availability of a large, freely redistributable set of high-quality annotated images is critical to allowing researchers in the area of automatic annotation, generic object recognition and concept detection to compare results. The recent introduction of the MIR Flickr dataset allows researchers such access. A dataset by itself is not enough, and a set of repeatable guidelines for performing evaluations that are comparable is required. In many cases it also is useful to compare the machine-learning components of different automatic annotation techniques using a common set of image features. This paper seeks to provide a solid, repeatable methodology and protocol for performing evaluations of automatic annotation software using the MIR Flickr dataset together with freely available tools for measuring performance in a controlled manner. This protocol is demonstrated through a set of experiments using a “semantic space” auto-annotator previously developed by the authors, in combination with a set of visual term features for the images that has been made publicly available for download. The paper also discusses how much training data is required to train the semantic space annotator with the MIR Flickr dataset. It is the hope of the authors that researchers will adopt this methodology and produce results from their own annotators that can be directly compared to those presented in this work.