In this paper we introduce a novel optimization framework for hierarchical data clustering and apply it to the problem of unsupervised texture segmentation. The proposed objective...
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
An overview is presented of the use of hierarchical spatial data structures such as the quadtree. They are based on the principle of recursive decomposition. The focus is on the r...
Objects in the world can be arranged into a hierarchy based on their semantic meaning (e.g. organism ? animal ? feline ? cat). What about defining a hierarchy based on the visual ...
Josef Sivic, Bryan C. Russell, Andrew Zisserman, W...
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...