4,760 research outputs found
Isolating contour information from arbitrary images
Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
A Neighborhood-preserving Graph Summarization
We introduce in this paper a new summarization method for large graphs. Our
summarization approach retains only a user-specified proportion of the
neighbors of each node in the graph. Our main aim is to simplify large graphs
so that they can be analyzed and processed effectively while preserving as many
of the node neighborhood properties as possible. Since many graph algorithms
are based on the neighborhood information available for each node, the idea is
to produce a smaller graph which can be used to allow these algorithms to
handle large graphs and run faster while providing good approximations.
Moreover, our compression allows users to control the size of the compressed
graph by adjusting the amount of information loss that can be tolerated. The
experiments conducted on various real and synthetic graphs show that our
compression reduces considerably the size of the graphs. Moreover, we conducted
several experiments on the obtained summaries using various graph algorithms
and applications, such as node embedding, graph classification and shortest
path approximations. The obtained results show interesting trade-offs between
the algorithms runtime speed-up and the precision loss.Comment: 17 pages, 10 figure
- …