Minimalist AdaBoost for blemish identification in potatoes

Barnes, Michael and Cielniak, Grzegorz and Duckett, Tom (2010) Minimalist AdaBoost for blemish identification in potatoes. In: International Conference on Computer Vision and Graphics 2010, Sep 20-22 2010, PJIIT - Polish-Japanese Institute of Information Technology, Warsaw, Poland.

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Official URL: http://www.springerlink.com/content/1871m28r0362l7...

Abstract

We present a multi-class solution based on minimalist Ad-
aBoost for identifying blemishes present in visual images of potatoes.
Using training examples we use Real AdaBoost to rst reduce the fea-
ture set by selecting ve features for each class, then train binary clas-
siers for each class, classifying each testing example according to the
binary classier with the highest certainty. Against hand-drawn ground
truth data we achieve a pixel match of 83% accuracy in white potatoes
and 82% in red potatoes. For the task of identifying which blemishes
are present in each potato within typical industry dened criteria (10%
coverage) we achieve accuracy rates of 93% and 94%, respectively.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6374 LNCS, Issue PART 1, 2010, Pages 209-216
Keywords:Potatoes, Computer Vision, Machine Learning, bmjtype
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:5517
Deposited By: Michael Barnes
Deposited On:11 May 2012 11:15
Last Modified:09 Jul 2013 15:06

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