Cited by 3,442Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval TechniquesAdvanced Neural Network Applications

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Automated Flower Classification over a Large Number of Classes

Nilsback, Maria-Elena, Zisserman, Andrew·2008·2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing

DOI 10.1109/icvgip.2008.47

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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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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
DOI: 10.1109/icvgip.2008.47
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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Automated Flower Classification over a Large Number of Classes

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