Towards Semantic Image Annotation With Keyword Disambiguation Using Semantic And Visual Knowledge
نویسندگان
چکیده
This paper deals with the semantic enrichment of automatic annotations of images. Since it partially tackles the Semantic Gap Problem, semantic image annotation has received a large attention in the recent years. Nevertheless, the results of existing image annotation approaches are still not sufficient. We propose an original approach combining a priori knowledge (in our case, the WordNet lexical resource) and visual knowledge to build sense-tagged keywords-based annotation. First, a graph-based approach assigns a bag-ofkeywords to a query image. Then, we propose to adapt a word sense disambiguation algorithm named SSI (Structural Semantic Interconnections), initially dedicated to text. We make two adaptations. First the grammar used in the SSI is modified to reflect the preponderance of semantic relations in image databases. Then, visual knowledge, including co-occurrence statistics in the visual domain and visual cues, is integrated. At last, a method to evaluate our approach is proposed.
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