The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Content-based image retrieval at the end of the early years
Open accessAbstract
Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333
Cited by
A survey of content-based image retrieval with high-level semantics
Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.045 · Cited by 1,696
Content-based multimedia information retrieval: State of the art and challenges
Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/1126004.1126005 · Cited by 1,528
Self-citationThe role of context in object recognition
Oliva, A., & Torralba, A. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.09.009 · Cited by 1,063
A Review of Data Fusion Techniques
Castanedo, F. (2013) · The Scientific World Journal · doi:10.1155/2013/704504 · Cited by 1,027
HashNet: Deep Learning to Hash by Continuation
Cao, Z., Long, M., Wang, J., & Yu, P. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.598 · Cited by 777
Metadata
- Authors
- Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 12
- Pages
- 1349-1380
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2130660124
- Cited by
- 6,056
- References
- 4
- FWCI
- 136.33
- Type
- journal article
- Language
- en
- Open access
- green
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- Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.895972
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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