The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Joint Summarization of Large-Scale Collections of Web Images and Videos for Storyline Reconstruction
Open accessAbstract
In this paper, we address the problem of jointly summarizing large sets of Flickr images and YouTube videos. Starting from the intuition that the characteristics of the two media types are different yet complementary, we develop a fast and easily-parallelizable approach for creating not only high-quality video summaries but also novel structural summaries of online images as storyline graphs. The storyline graphs can illustrate various events or activities associated with the topic in a form of a branching network. The video summarization is achieved by diversity ranking on the similarity graphs between images and video frames. The reconstruction of storyline graphs is formulated as the inference of sparse time-varying directed graphs from a set of photo streams with assistance of videos. For evaluation, we collect the datasets of 20 outdoor activities, consisting of 2.7M Flickr images and 16K YouTube videos. Due to the large-scale nature of our problem, we evaluate our algorithm via crowdsourcing using Amazon Mechanical Turk. In our experiments, we demonstrate that the proposed joint summarization approach outperforms other baselines and our own methods using videos or images only.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Cited by
TVSum: Summarizing web videos using titles
Song, Y., Vallmitjana, J., Stent, A., & Jaimes, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299154 · Cited by 623
Category-Specific Video Summarization
Potapov, D., Douze, M., Harchaoui, Z., & Schmid, C. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_35 · Cited by 481
Video summarization by learning submodular mixtures of objectives
Gygli, M., Grabner, H., & Van Gool, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298928 · Cited by 397
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.274 · Cited by 373
Metadata
- Authors
- Kim, G., Sigal, L., & Xing, E. P.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 4225-4232
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1976806664
- Cited by
- 166
- References
- 1
- FWCI
- 12.43
- Type
- conference paper
- Language
- en
- Open access
- green
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