This paper studies the effect of covariance regularization for classific ation of high-dimensional data. This is done by fitting a mixture of Gaussians with a regularized covaria...
Daniel L. Elliott, Charles W. Anderson, Michael Ki...
Image-analysis methods play an important role in helping detect brain changes in and diagnosis of Alzheimer's Disease (AD). In this paper, we propose an automatic unsupervised...
Gemma Roig received the BS degree in image and sound engineering, the MS degree in telecommunication engineering and the MS in telecommunication networks from La Salle, Universitat...
User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is s...
Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schem...
In this paper, we present a multi-label sparse coding
framework for feature extraction and classification within
the context of automatic image annotation. First, each image
is ...
Changhu Wang (University of Science and Technology...
We study the problem of object classification when training
and test classes are disjoint, i.e. no training examples of
the target classes are available. This setup has hardly be...
Christoph H. Lampert, Hannes Nickisch, Stefan Harm...
Given an image, we propose a hierarchical generative
model that classifies the overall scene, recognizes and segments
each object component, as well as annotates the image
with ...
This paper presents a unified framework for object detection,
segmentation, and classification using regions. Region
features are appealing in this context because: (1) they enco...
Chunhui Gu, Joseph J. Lim, Pablo Arbelaez, Jitendr...