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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On the Flower dataset, using class tokens from all blocks gives a relatively better improvement (only 1.2%) compared to using class tokens from the last four blocks.
“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]
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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.”
BackgroundMethodInfluentialPairwise Rotation Invariant Co-Occurrence Local Binary Pattern
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- 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
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.
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