Maria-Elena Nilsback and Andrew Zisserman



Overview

We have created a 102 category dataset, consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images. The details of the categories and the number of images for each class can be found on this category statistics page.

The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories. The dataset is visualized using isomap with shape and colour features.

Downloads

The data needed for evaluation are:

  1. Dataset images
  2. Image segmentations
  3. &Chi2 distances - As used in the ICVGIP 2008 publication.
  4. The image labels
  5. The data splits

The README file explains everything.

Visualization of the dataset

We visualize the categories in the dataset using SIFT features as shape descriptors and HSV as colour descriptor. The images are randomly sampled from the category.

Shape Isomap
Isomap using shape features
Colour Isomap
Isomap using shape features

Publications


Nilsback, M-E. and Zisserman, A.
Automated flower classification over a large number of classes  
Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)