We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
A number of recent systems for unsupervised featurebased learning of object models take advantage of cooccurrence: broadly, they search for clusters of discriminative features tha...
In this paper, we present a framework for clustering and classifying cheque images according to their payee-line content. The features used in the clustering and classificationpro...
Ossama El Badawy, Mahmoud R. El-Sakka, Khaled Hass...
In this paper, we propose an efficient hierarchical method for extracting invariant texture features using the Gabor wavelets and Radon transform parameters. The proposed method a...
— In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination ...