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Face image super-resolution using 2D CCA
In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V
Expanded Parts Model for Semantic Description of Humans in Still Images
We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.Comment: Accepted for publication in IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI
Migration as Submodular Optimization
Migration presents sweeping societal challenges that have recently attracted
significant attention from the scientific community. One of the prominent
approaches that have been suggested employs optimization and machine learning
to match migrants to localities in a way that maximizes the expected number of
migrants who find employment. However, it relies on a strong additivity
assumption that, we argue, does not hold in practice, due to competition
effects; we propose to enhance the data-driven approach by explicitly
optimizing for these effects. Specifically, we cast our problem as the
maximization of an approximately submodular function subject to matroid
constraints, and prove that the worst-case guarantees given by the classic
greedy algorithm extend to this setting. We then present three different models
for competition effects, and show that they all give rise to submodular
objectives. Finally, we demonstrate via simulations that our approach leads to
significant gains across the board.Comment: Simulation code is available at https://github.com/pgoelz/migration
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