We present a parameter free approach that utilizes multiple cues for image segmentation. Beginning with an image, we execute a sequence of bottom-up aggregation steps in which pix...
This paper presents a novel local image descriptor that is robust to general image deformations. A limitation with traditional image descriptors is that they use a single support ...
In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is part...
In this paper, we present a novel approach to contentsbased image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are...
This paper presents a new approach to discriminative modeling for classi cation and labeling. Our method, called Boosting on Multilevel Aggregates (BMA), adds a new class of hiera...