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2017 | OriginalPaper | Buchkapitel

Using Object Detection, NLP, and Knowledge Bases to Understand the Message of Images

verfasst von : Lydia Weiland, Ioana Hulpus, Simone Paolo Ponzetto, Laura Dietz

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

With the increasing amount of multimodal content from social media posts and news articles, there has been an intensified effort towards conceptual labeling and multimodal (topic) modeling of images and of their affiliated texts. Nonetheless, the problem of identifying and automatically naming the core abstract message (gist) behind images has received less attention. This problem is especially relevant for the semantic indexing and subsequent retrieval of images. In this paper, we propose a solution that makes use of external knowledge bases such as Wikipedia and DBpedia. Its aim is to leverage complex semantic associations between the image objects and the textual caption in order to uncover the intended gist. The results of our evaluation prove the ability of our proposed approach to detect gist with a best MAP score of 0.74 when assessed against human annotations. Furthermore, an automatic image tagging and caption generation API is compared to manually set image and caption signals. We show and discuss the difficulty to find the correct gist especially for abstract, non-depictable gists as well as the impact of different types of signals on gist detection quality.

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Vorheriges Kapitel Unsupervised Multiple Object Cosegmentation via Ensemble MIML Learning

Nächstes Kapitel Video Search via Ranking Network with Very Few Query Exemplars

https://www.microsoft.com/cognitive-services/en-us/computer-vision-api.

dataset and gold standard: https://github.com/gistDetection/GistDataset.

http://lemurproject.org/ranklib.php.

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Titel: Using Object Detection, NLP, and Knowledge Bases to Understand the Message of Images
verfasst von: Lydia Weiland
Ioana Hulpus
Simone Paolo Ponzetto
Laura Dietz
Verlag: Springer International Publishing
Buch: MultiMedia Modeling
Print ISBN: 978-3-319-51813-8

Electronic ISBN: 978-3-319-51814-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-51814-5_34

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