A Pipeline Approach to Image Auto-Tagging Refinement
Olga Kanishcheva, Galia Angelova · 2015
This paper describes research aiming to refine the tags that are assigned automatically to images by an industrial auto-tagging system (Imagga). The present annotation contains English keyword tags proposed by the original auto-tagging algorithms which assign to each image a set of keywords corresponding to shapes and colors that are recognized in the image. After that the system extends the original metadata by adding the keywords of similar images found in external resources. Thus, on the average, 40 tags with various origin and relevance ranking are assigned automatically to each image. In this paper we propose at first to refine the keywords using Natural Language Processing (NLP) post-editing tools: to recognize the morphological variants, derivative tags, synonyms and phrases. Further we propose to use conceptual similarity metrics and to identify tags that might be viewed as semantically redundant. The experiments described here are performed on a subset of images selected from ImageNet.