The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Content-based image retrieval: approaches and trends of the new age
Abstract
The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics.
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Citations
Cites
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
Cited by
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
“…resulted in the significant challenge of how to efficiently manage the large data collections, particularly so that the data of interest can be accessed quickly Content based image retrieval (CBIR) is a useful technique for the fast retrieval of images of interest from a large-scale dataset [1–4].”
Method
Metadata
- Authors
- Datta, R., Li, J., & Wang, J. Z.
- Venue
- Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
- Publisher
- ACM
- Published
- 2005-11-10
- Pages
- 253-262
- OpenAlex
- W2167407098
- Cited by
- 435
- References
- 1
- FWCI
- 25.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Content-based image retrieval: approaches and trends of the new age. https://app.cochat.ai/papers/10.1145/1101826.1101866
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- Datta, R., Li, J., & Wang, J. Z. (2005). Content-based image retrieval: approaches and trends of the new age. Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval. https://doi.org/10.1145/1101826.1101866
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
