The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics
Abstract
This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporate a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts. We present two ways to train the three-view embedding: supervised, with the third view coming from ground-truth labels or search keywords; and unsupervised, with semantic themes automatically obtained by clustering the tags. To ensure high accuracy for retrieval tasks while keeping the learning process scalable, we combine multiple strong visual features and use explicit nonlinear kernel mappings to efficiently approximate kernel CCA. To perform retrieval, we use a specially designed similarity function in the embedded space, which substantially outperforms the Euclidean distance. The resulting system produces compelling qualitative results and outperforms a number of two-view baselines on retrieval tasks on three large-scale Internet image datasets.
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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
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Self-citationSUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J., Perronnin, F., & Csurka, G. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_35 · Cited by 281
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Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444
Self-citationAdversarial Cross-Modal Retrieval
Wang, B., Yang, Y., Xu, X., Hanjalic, A., & Shen, H. T. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123326 · Cited by 779
Learning Deep Structure-Preserving Image-Text Embeddings
Wang, L., Li, Y., & Lazebnik, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.541 · Cited by 760
Self-citationLarge-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
Zhang, D., & Li, W.-J. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8995 · Cited by 616
End-to-End Learning of Visual Representations From Uncurated Instructional Videos
Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00990 · Cited by 580
Metadata
- Authors
- Gong, Y., Ke, Q., Isard, M., & Lazebnik, S.
- Venue
- International Journal of Computer Vision
- Published
- 2013-10-02
- Volume
- 106
- Issue
- 2
- Pages
- 210-233
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2070753207
- Cited by
- 540
- References
- 6
- FWCI
- 42.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gong, Y., Ke, Q., Isard, M., & Lazebnik, S. (2014). A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics. International Journal of Computer Vision. https://doi.org/10.1007/s11263-013-0658-4
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