Fusing color and shape for bag-of-words based object recognition
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
The pooled NBNN kernel: beyond image-to-class and image-to-image
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
The multi-feature information bottleneck with application to unsupervised image categorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A framework for selection and fusion of pattern classifiers in multimedia recognition
Pattern Recognition Letters
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Bag-of-words-based image classification approaches mostly rely on low level local shape features. However, it has been shown that combining multiple cues such as color, texture, or shape is a challenging and promising task which can improve the classification accuracy. Most of the state-of-the-art feature fusion methods usually aim to weight the cues without considering their statistical dependence in the application at hand. In this paper, we present a new logistic regression-based fusion method, called LRFF, which takes advantage of the different cues without being tied to any of them. We also design a new marginalized kernel by making use of the output of the regression model. We show that such kernels, surprisingly ignored so far by the computer vision community, are particularly well suited to achieve image classification tasks. We compare our approach with existing methods that combine color and shape on three datasets. The proposed learning-based feature fusion process clearly outperforms the state-of-the art fusion methods for image classification.