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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
dataset and gold standard: https://github.com/gistDetection/GistDataset.
- 3.
References
Barbu, A., Bridge, A., Burchill, Z., Coroian, D., Dickinson, S.J., Fidler, S., Zhang, Z.: Video in sentences out. In: UAI, pp. 102–112 (2012)
Bernardi, R., Cakici, R., Elliott, D., Erdem, A., Erdem, E., Ikizler-Cinbis, N., Plank, B.: Automatic description generation from images: a survey of models, datasets, and evaluation measures. arXiv preprint arXiv:1601.03896 (2016)
Bruni, E., Uijlings, J., Baroni, M., Sebe, N.: Distributional semantics with eyes: using image analysis to improve computational representations of word meaning. In: MM, pp. 1219–1228 (2012)
Das, P., Srihari, R.K., Corso, J.J.: Translating related words to videos and back through latent topics. In: WSDM, pp. 485–494 (2013)
Das, P., Xu, C., Doell, R.F., Corso, J.J.: A thousand frames in just a few words: lingual description of videos through latent topics and sparse object stitching. In: CVPR, pp. 2634–2641 (2013)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
Elliott, D., Keller, F.: Image description using visual dependency representations. In: EMNLP, pp. 1292–1302 (2013)
Fang, H., Gupta, S., Iandola, F.N., Srivastava, R., Deng, L., Dollár, P., Zweig, G.: From captions to visual concepts and back. In: CVPR, pp. 1473–1482 (2015)
Farhadi, A., Hejrati, M., Sadeghi, M.A., Young, P., Rashtchian, C., Hockenmaier, J., Forsyth, D.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 15–29. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_2
Feng, Y., Lapata, M.: How many words is a picture worth? Automatic caption generation for news images. In: ACL, pp. 1239–1249 (2010)
Feng, Y., Lapata, M.: Topic models for image annotation and text illustration. In: NAACL-HLT, pp. 831–839 (2010)
Fleiss, J., et al.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378–382 (1971)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR (2013)
Gupta, A., Verma, Y., Jawahar, C.V.: Choosing linguistics over vision to describe images. In: AAAI, pp. 606–612 (2012)
Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: data, models and evaluation metrics. IJCAI 47, 853–899 (2013)
Hulpus, I., Hayes, C., Karnstedt, M., Greene, D.: Unsupervised graph-based topic labelling using DBpedia. In: Proceedings of the WSDM 2013, pp. 465–474 (2013)
Hulpuş, I., Prangnawarat, N., Hayes, C.: Path-based semantic relatedness on linked data and its use to word and entity disambiguation. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 442–457. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25007-6_26
Jin, Y., Khan, L., Wang, L., Awad, M.: Image annotations by combining multiple evidence & WordNet. In: MM, pp. 706–715 (2005)
Karpathy, A., Li, F.F.: Deep visual-semantic alignments for generating image descriptions. In: CVPR, pp. 3128-3137. IEEE Computer Society (2015)
Krishnamoorthy, N., Malkarnenkar, G., Mooney, R., Saenko, K., Guadarrama, S.: Generating natural-language video descriptions using text-mined knowledge. In: AAAI (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Kulkarni, G., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A.C., Berg, T.L.: Baby talk: understanding and generating image descriptions. In: CVPR, pp. 1601–1608 (2011)
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10602-1_48
Navigli, R., Ponzetto, S.P.: Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Nikolaos Aletras, M.S.: Computing similarity between cultural heritage items using multimodal features. In: LaTeCH at EACL, pp. 85–92 (2012)
O’Neill, S., Nicholson-Cole, S.: Fear won’t do it: promoting positive engagement with climate change through imagery and icons. Sci. Commun. 30(3), 355–379 (2009)
O’Neill, S., Smith, N.: Climate change and visual imagery. Wiley Interdisc. Rev.: Clim. Change 5(1), 73–87 (2014)
Ordonez, V., Kulkarni, G., Berg, T.L.: Im2text: describing images using 1 million captioned photographs. In: NIPS (2011)
Ortiz, L.G.M., Wolff, C., Lapata, M.: Learning to interpret and describe abstract scenes. In: NAACL HLT 2015, pp. 1505–1515 (2015)
Rashtchian, C., Young, P., Hodosh, M., Hockenmaier, J.: Collecting image annotations using Amazon’s mechanical turk. In: CSLDAMT at NAACL HLT (2010)
Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G.R., Levy, R., Vasconcelos, N.: A new approach to cross-modal multimedia retrieval. In: MM, pp. 251–260 (2010)
Socher, R., Fei-Fei, L.: Connecting modalities: semi-supervised segmentation and annotation of images using unaligned text corpora. In: CVPR (2010)
Socher, R., Karpathy, A., Le, Q.V., Manning, C.D., Ng, A.Y.: Grounded compositional semantics for finding and describing images with sentences. ACL 2, 207–218 (2014)
Wang, C., Yang, H., Che, X., Meinel, C.: Concept-based multimodal learning for topic generation. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8935, pp. 385–395. Springer, Heidelberg (2015). doi:10.1007/978-3-319-14445-0_33
Weiland, L., Hulpus, I., Ponzetto, S.P., Dietz, L.: Understanding the message of images with knowledge base traversals. In: Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval, ICTIR 2016, Newark, DE, USA, 12–16 September 2016, pp. 199–208 (2016)
Yang, Y., Teo, C.L., Daumé III, H., Aloimonos, Y.: Corpus-guided sentence generation of natural images. In: EMNLP, pp. 444–454 (2011)
Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. In: ACL, pp. 67–78 (2014)
Acknowledgements
This work is funded by the RiSC programme of the Ministry of Science, Research and the Arts Baden-Wuerttemberg, and used computational resources offered from the bwUni-Cluster within the framework program bwHPC. Furthermore, this work was in part funded through the Elitepostdoc program of the BW-Stiftung and the University of New Hampshire.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Weiland, L., Hulpus, I., Ponzetto, S.P., Dietz, L. (2017). Using Object Detection, NLP, and Knowledge Bases to Understand the Message of Images. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10133. Springer, Cham. https://doi.org/10.1007/978-3-319-51814-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-51814-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51813-8
Online ISBN: 978-3-319-51814-5
eBook Packages: Computer ScienceComputer Science (R0)