We consider the problem of finding association rules that make nearly optimal binary segmentations of huge categorical databases. The optimality of segmentation is defined by an o...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
In this paper we propose a novel framework for 3D object categorization. The object is modeled it in terms of its sub-parts as an histogram of 3D visual word occurrences. We introd...
Roberto Toldo, Umberto Castellani, Andrea Fusiello
This paper presents a framework for using high-level visual information to enhance the performance of automatic color constancy algorithms. The approach is based on recognizing spe...
Esa Rahtu, Jarno Nikkanen, Juho Kannala, Leena Lep...
Dynamic textures (DT) are videos of non-rigid dynamical objects, such as fire and waves, which constantly change their shape and appearance over time. Most of the prior work on DT ...