The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Image retrieval: Ideas, influences, and trends of the new age
Abstract
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
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
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Optimal multimodal fusion for multimedia data analysis
Wu, Y., Chang, E. Y., Chang, K. C.-C., & Smith, J. R. (2004) · Proceedings of the 12th annual ACM international conference on Multimedia · doi:10.1145/1027527.1027665 · Cited by 229
Identifying biological concepts from a protein-related corpus with a probabilistic topic model
Zheng, B., McLean, D. C., & Lu, X. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-58 · Cited by 68
Cited by
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor
Ren, Z., Yuan, J., Meng, J., & Zhang, Z. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2246148 · Cited by 746
Small codes and large image databases for recognition
Torralba, A., Fergus, R., & Weiss, Y. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587633 · Cited by 727
Deep hashing for compact binary codes learning
Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298862 · Cited by 545
Metadata
- Authors
- Datta, R., Joshi, D., Li, J., & Wang, J. Z.
- Venue
- ACM Computing Surveys
- Published
- 2008-04
- Volume
- 40
- Issue
- 2
- Pages
- 1-60
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2111993661
- Cited by
- 3,040
- References
- 7
- FWCI
- 455.35
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Image retrieval: Ideas, influences, and trends of the new age. https://app.cochat.ai/papers/10.1145/1348246.1348248
- The paper
- Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys. https://doi.org/10.1145/1348246.1348248
Data sources
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.
