Abstract
Automatic identification of intended tag meanings is a challenge in large image collections where human authors assign tags inspired by emotional or professional motivations. Algorithms for automatic tag disambiguation need “golden” collections of manually created tags to establish baselines for accuracy assessment. Here we show how to use the MIRFLICKR-25000 collection to evaluate the performance of our algorithm for tag sense disambiguation which identifies meanings of image tags based on WordNet or Wikipedia. We present three different types of observations on the disambiguated tags: (i) accuracy evaluation, (ii) evaluation of the semantic similarity of the individual tags with the image category and (iii) the semantic similarity of an image tagset to the image category, using different word embedding models for the latter two. We show how word embeddings create a specific baseline so the results can be compared. The accuracy we achieve is 78.6%.
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Kanishcheva, O., Nikolova, I., Angelova, G. (2018). Evaluation of Automatic Tag Sense Disambiguation Using the MIRFLICKR Image Collection. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_6
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