1,090 research outputs found
A Harmonic Extension Approach for Collaborative Ranking
We present a new perspective on graph-based methods for collaborative ranking
for recommender systems. Unlike user-based or item-based methods that compute a
weighted average of ratings given by the nearest neighbors, or low-rank
approximation methods using convex optimization and the nuclear norm, we
formulate matrix completion as a series of semi-supervised learning problems,
and propagate the known ratings to the missing ones on the user-user or
item-item graph globally. The semi-supervised learning problems are expressed
as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and
can be discretized by a point integral method. We show that our approach does
not impose a low-rank Euclidean subspace on the data points, but instead
minimizes the dimension of the underlying manifold. Our method, named LDM (low
dimensional manifold), turns out to be particularly effective in generating
rankings of items, showing decent computational efficiency and robust ranking
quality compared to state-of-the-art methods
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
This paper discusses the challenges of evaluating deblurring-methods quality
and proposes a reduced-reference metric based on machine learning. Traditional
quality-assessment metrics such as PSNR and SSIM are common for this task, but
not only do they correlate poorly with subjective assessments, they also
require ground-truth (GT) frames, which can be difficult to obtain in the case
of deblurring. To develop and evaluate our metric, we created a new motion-blur
dataset using a beam splitter. The setup captured various motion types using a
static camera, as most scenes in existing datasets include blur due to camera
motion. We also conducted two large subjective comparisons to aid in metric
development. Our resulting metric requires no GT frames, and it correlates well
with subjective human perception of blur
No-reference Point Cloud Geometry Quality Assessment Based on Pairwise Rank Learning
Objective geometry quality assessment of point clouds is essential to
evaluate the performance of a wide range of point cloud-based solutions, such
as denoising, simplification, reconstruction, and watermarking. Existing point
cloud quality assessment (PCQA) methods dedicate to assigning absolute quality
scores to distorted point clouds. Their performance is strongly reliant on the
quality and quantity of subjective ground-truth scores for training, which are
challenging to gather and have been shown to be imprecise, biased, and
inconsistent. Furthermore, the majority of existing objective geometry quality
assessment approaches are carried out by full-reference traditional metrics. So
far, point-based no-reference geometry-only quality assessment techniques have
not yet been investigated. This paper presents PRL-GQA, the first pairwise
learning framework for no-reference geometry-only quality assessment of point
clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a
siamese deep architecture, which takes as input a pair of point clouds and
outputs their rank order. Each siamese architecture branch is a geometry
quality assessment network (GQANet), which is designed to extract multi-scale
quality-aware geometric features and output a quality index for the input point
cloud. Then, based on the predicted quality indexes, a pairwise rank learning
module is introduced to rank the relative quality of a pair of degraded point
clouds.Extensive experiments demonstrate the effectiveness of the proposed
PRL-GQA framework. Furthermore, the results also show that the fine-tuned
no-reference GQANet performs competitively when compared to existing
full-reference geometry quality assessment metrics
Automatic Document Image Binarization using Bayesian Optimization
Document image binarization is often a challenging task due to various forms
of degradation. Although there exist several binarization techniques in
literature, the binarized image is typically sensitive to control parameter
settings of the employed technique. This paper presents an automatic document
image binarization algorithm to segment the text from heavily degraded document
images. The proposed technique uses a two band-pass filtering approach for
background noise removal, and Bayesian optimization for automatic
hyperparameter selection for optimal results. The effectiveness of the proposed
binarization technique is empirically demonstrated on the Document Image
Binarization Competition (DIBCO) and the Handwritten Document Image
Binarization Competition (H-DIBCO) datasets
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