The DOI matches its Crossref record; 18 of the 20 references it cites were found in a registry or an index.
Automated Flower Classification over a Large Number of Classes
Abstract
We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.
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“We find some exception to this on the Flower dataset [38] where using class tokens from all blocks have relatively better improvement (only 1.2%), compared to the class tokens from the last four blocks (Table 5).”
From Intriguing Properties of Vision Transformers (2021) · cites this paper as [38]
Citations
Cites
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Histograms of Oriented Gradients for Human Detection
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Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
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(2007) · CaltechAUTHORS (California Institute of Technology) · Cited by 2,274
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Multiple kernel learning, conic duality, and the SMO algorithm
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Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423
Self-citationImage 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
Self-citationSVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
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A Visual Vocabulary for Flower Classification
Nilsback, M.-E., & Zisserman, A. (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.2006.42 · Cited by 876
Self-citationLearning The Discriminative Power-Invariance Trade-Off
Varma, M., & Ray, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408875 · Cited by 524
Recognition by association via learning per-exemplar distances
Malisiewicz, T., & Efros, A. A. (2008) · 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.2008.4587462 · Cited by 135
Indexing flower patent images using domain knowledge
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Delving into the whorl of flower segmentation
Nilsback, M.-E., & Zisserman, A. (2007) · British Machine Vision Conference (BMVC) · doi:10.5244/c.21.54 · Cited by 83
Self-citationAutomatic recognition of blooming flowers
Saitoh, T., Aoki, K., & Kaneko, T. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1333997 · Cited by 57
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2 of the 20 references couldn’t be matched to a known paper.
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
“We transfer the models trained on these dataset to several benchmark tasks: ImageNet on the original validation labels and the cleaned-up ReaL labels (Beyer et al., 2020), CIFAR-10/100 (Krizhevsky, 2009), Oxford-IIIT Pets (Parkhi et al., 2012), and Oxford Flowers-102 (Nilsback & Zisserman, 2008).”
MethodEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638
“We use ImageNet-1k [60] (“Inet”), Places205 [84], PASCAL VOC [24] and Oxford-102 flowers (“FLOWERS”) [46].”
MethodCNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
3D Object Representations for Fine-Grained Categorization
Krause, J., Stark, M. M., Deng, J., & Fei-Fei, L. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,666
Metadata
- Authors
- Nilsback, M.-E., & Zisserman, A.
- Affiliations
- University of Oxford (GB)
- ORCID
- Zisserman, Andrew
- Venue
- 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
- Publisher
- IEEE
- Published
- 2008-12
- Pages
- 722-729
- OpenAlex
- W2533598788
- Semantic Scholar
- 02b28f3b71138a06e40dbd614abf8568420ae183
- Cited by
- 3,442
- References
- 20
- Citations by year
- 2026: 304 · 2025: 516 · 2024: 502 · 2023: 447 · 2022: 316 · 2021: 349 · 2020: 223 · 2019: 212 · 2018: 125 · 2017: 96 · 2016: 93 · 2015: 70 · 2014: 56 · 2013: 41 · 2012: 34
- FWCI
- 15.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Indian Conference on Computer Vision, Graphics and Image Processing (publishedVersion)
- Funders
- Microsoft Research
- Royal Academy of Engineering
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- flower classification, large number of classes, feature combination, multiple kernel learning, support vector machine, color features
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