Incremental Learning of NCM Forests for Large-Scale Image Classification
Open accessAbstract
In recent years, large image data sets such as "ImageNet", "TinyImages" or ever-growing social networks like "Flickr" have emerged, posing new challenges to image classification that were not apparent in smaller image sets. In particular, the efficient handling of dynamically growing data sets, where not only the amount of training images, but also the number of classes increases over time, is a relatively unexplored problem. To remedy this, we introduce Nearest Class Mean Forests (NCMF), a variant of Random Forests where the decision nodes are based on nearest class mean (NCM) classification. NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes. To this end, we propose and compare several approaches to incorporate data from new classes, so as to seamlessly extend the previously trained forest instead of re-training them from scratch. In our experiments, we show that NCMFs trained on small data sets with 10 classes can be extended to large data sets with 1000 classes without significant loss of accuracy compared to training from scratch on the full data.
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
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.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
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 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Self-citationScalable Recognition with a Vocabulary Tree
Nistér, D., & Stewénius, H. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.264 · Cited by 3,599 · see citing works
SUN 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 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714 · see citing works
Mining high-speed data streams
Domingos, P., & Hulten, G. (2000) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347107 · Cited by 2,213 · see citing works
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 · see citing works
Random sampling with a reservoir
Vitter, J. S. (1985) · ACM Transactions on Mathematical Software · doi:10.1145/3147.3165 · Cited by 1,789 · see citing works
Image Classification using Random Forests and Ferns
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409066 · Cited by 1,254 · see citing works
Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457447 · Cited by 502 · see citing works
What Does Classifying More Than 10,000 Image Categories Tell Us?
Deng, J., Berg, A. C., Li, K., & Fei-Fei, L. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_6 · Cited by 487 · see citing works
Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J. J., Perronnin, F., & Csurka, G. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.83 · Cited by 426 · see citing works
Large-scale image classification: Fast feature extraction and SVM training
Lin, Y., Lv, F., Shenghuo Zhu, B., Yan, S., Cour, T., Yu, K., Cao, L., & Huang, T. S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995477 · Cited by 388 · see citing works
Tabula rasa: Model transfer for object category detection
Aytar, Y., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126504 · Cited by 339 · see citing works
Learning to share visual appearance for multiclass object detection
Salakhutdinov, R., Torralba, A. B., & Tenenbaum, J. B. (2011) · doi:10.1109/cvpr.2011.5995720 · Cited by 326 · see citing works
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
Rohrbach, M., Stark, M. M., & Schiele, B. (2011) · doi:10.1109/cvpr.2011.5995627 · Cited by 309 · see citing works
Combining randomization and discrimination for fine-grained image categorization
Yao, B., Khosla, A., & Fei-Fei, L. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995368 · Cited by 291 · see citing works
High-dimensional signature compression for large-scale image classification
Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272 · see citing works
Safety in numbers: Learning categories from few examples with multi model knowledge transfer
Tommasi, T., Orabona, F., & Barbara Caputo, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540064 · Cited by 218 · see citing works
Hough-based tracking of non-rigid objects
Godec, M., Roth, P. M., & Bischof, H. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126228 · Cited by 202 · see citing works
Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition
Deng, J., Krause, J., Berg, A., & Fei-Fei, L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248086 · Cited by 175 · see citing works
Good Practice in Large-Scale Learning for Image Classification
Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.146 · Cited by 156 · see citing works
Shared parts for deformable part-based models
Ott, P., & Everingham, M. (2011) · doi:10.1109/cvpr.2011.5995357 · Cited by 96 · see citing works
Yao, A., Gall, J., Leistner, C., & Gool, L. V. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248060 · Cited by 88 · see citing works
Self-citationA shape-based object class model for knowledge transfer
Stark, M. M., Goesele, M., & Schiele, B. (2009) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459231 · Cited by 83 · see citing works
Large-scale knowledge transfer for object localization in ImageNet
Guillaumin, M., & Ferrari, V. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248055 · Cited by 72 · see citing works
Self-citationAdaptive Vocabulary Forests br Dynamic Indexing and Category Learning
YEH, T. P., Lee, J., & Darrell, T. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409053 · Cited by 49 · see citing works
An incremental extremely random forest classifier for online learning and tracking
Wang, A., Wan, G., Cheng, Z., & Li, S. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414559 · Cited by 44 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [27]27. A. Yao, J. Gall, C. Leistner, and L. Van Gool. Interac- tive object detection. In CVPR, 2012.
Cited by
Sorted by citations
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988 · see citing works
Towards Open Set Deep Networks
Bendale, A., & Boult, T. E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.173 · Cited by 1,799 · see citing works
Incremental Learning of Object Detectors without Catastrophic Forgetting
Shmelkov, K., Schmid, C., & Alahari, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.368 · Cited by 559 · see citing works
- Authors
- Ristin, M., Guillaumin, M., Gall, J., & Van Gool, L.
- Affiliations
- ETH Zurich (CH)
- ORCID
- Ristin, Marko
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3654-3661
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2007556421
- Cited by
- 82
- References
- 29
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 8 · 2021: 7 · 2020: 9 · 2019: 6 · 2018: 12 · 2017: 12 · 2016: 8 · 2015: 7 · 2014: 1
- FWCI
- 5.50
- Type
- conference paper
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Deutsche Forschungsgemeinschaft (GA 1927/1-1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- incremental learning, random forest, image classification, large-scale image databases, ImageNet, Flickr
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
