DocumentCode :
2708784
Title :
Spatial pyramids and two-layer stacking SVM classifiers for image categorization: A comparative study
Author :
Abdullah, Azizi ; Veltkamp, Remco C. ; Wiering, Marco A.
Author_Institution :
Dept. of Inf. & Comput. Sci., Utrecht Univ., Utrecht, Netherlands
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
5
Lastpage :
12
Abstract :
Recent research in image recognition has shown that combining multiple descriptors is a very useful way to improve classification performance. Furthermore, the use of spatial pyramids that compute descriptors at multiple spatial resolution levels generally increases the discriminative power of the descriptors. In this paper we focus on combination methods that combine multiple descriptors at multiple spatial resolution levels. A possible problem of the naive solution to create one large input vector for a machine learning classifier such as a support vector machine, is that the input vector becomes of very large dimensionality, which can increase problems of overfitting and hinder generalization performance. Therefore we propose the use of stacking support vector machines where at the first layer each support vector machine receives the input constructed by each single descriptor and is trained to compute the right output class. A second layer support vector machine is then used to combine the class probabilities of all trained first layer support vector models to learn the right output class given these reduced input vectors. We have performed experiments on 20 classes from the Caltech object database with 10 different single descriptors at 3 different resolutions. The results show that our 2-layer stacking approach outperforms the naive approach that combines all descriptors directly in a very large single input vector.
Keywords :
image classification; image recognition; image resolution; learning (artificial intelligence); probability; support vector machines; image categorization; image classification; machine learning classifier; multiple spatial resolution; naive solution; probability; spatial pyramid; support vector machine; two-layer stacking; Artificial intelligence; Histograms; Image recognition; Machine learning; Machine learning algorithms; Neural networks; Spatial resolution; Stacking; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178743
Filename :
5178743
Link To Document :
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