The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Randomized Clustering Forests for Image Classification
Open accessAbstract
Some of the most effective recent methods for content-based image classification work by quantizing image descriptors, and accumulating histograms of the resulting visual word codes. Large numbers of descriptors and large codebooks are required for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests ensembles of randomly created clustering trees and show that they provide more accurate results, much faster training and testing, and good resistance to background clutter. Second, an efficient image classification method is proposed. It combines ERC-Forests and saliency maps very closely with the extraction of image information. For a given image, a classifier builds a saliency map online and uses it to classify the image. We show in several state-of-the-art image classification tasks that this method can speed up the classification process enormously. Finally, we show that the proposed ERC-Forests can also be used very successfully for learning distance between images. The distance computation algorithm consists of learning the characteristic differences between local descriptors sampled from pairs of same or different objects. These differences are vector quantized by ERC-Forests and the similarity measure is computed from this quantization. The similarity measure has been evaluated on four very different datasets and always outperforms the state-of-the-art competitive approaches.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
Learning a Similarity Metric Discriminatively, with Application to Face Verification
Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036
(2024) · Physics Subject Headings (PhySH) · doi:10.29172/7c2a6982-6d72-4cd8-bba6-2fccb06a7011 · Cited by 3,857
Scalable Recognition with a Vocabulary Tree
Nister, D., & Stewenius, H. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.264 · Cited by 3,579
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Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Food-101 – Mining Discriminative Components with Random Forests
Bossard, L., Guillaumin, M., & Van Gool, L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_29 · Cited by 2,072
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Metadata
- Authors
- Moosmann, F., Nowak, E., & Jurie, F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-09
- Volume
- 30
- Issue
- 9
- Pages
- 1632-1646
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2160978182
- Cited by
- 287
- References
- 58
- FWCI
- 35.80
- Type
- journal article
- Language
- en
- Open access
- green
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