PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A visual vocabulary for flower classification
M-E. Nilsback and Andrew Zisserman
In: CVPR 2006, 17-22 June 2006, New York, USA.


We investigate to what extent `bag of visual words' models can be used to distinguish categories which have significant visual similarity. To this end we develop and optimize a nearest neighbour classifier architecture, which is evaluated on a very challenging database of flower images. The flower categories are chosen to be indistinguishable on colour alone (for example), and have considerable variation in shape, scale, and viewpoint. We demonstrate that by developing a visual vocabulary that explicitly represents the various aspects (colour, shape, and texture) that distinguish one flower from another, we can overcome the ambiguities that exist between flower categories. The novelty lies in the vocabulary used for each aspect, and how these vocabularies are combined into a final classifier. The various stages of the classifier (vocabulary selection and combination) are each optimized on a validation set. Results are presented on a dataset of 1360 images consisting of 17 flower species. It is shown that excellent performance can be achieved, far surpassing standard baseline algorithms using (for example) colour cues alone.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2116
Deposited By:Mudigonda Pawan Kumar
Deposited On:02 June 2006