6,337 research outputs found
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
Automated Top View Registration of Broadcast Football Videos
In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
Learning with Weak Annotations for Text in the Wild Detection and Recognition
V tĂ©to práci pĹ™edstavujeme metodu vyuĹľĂvajĂcĂ slabÄ› anotovanĂ© obrázky pro vylepšenĂ systĂ©mĹŻ pro extrakci textu. Slabá antoace spoÄŤĂvá v seznamu textĹŻ, kterĂ© se v danĂ©m obrázku mohou vyskytovat, ale nevĂme kde. Metoda pouĹľĂvá libovolnĂ˝ existujĂcĂ systĂ©m pro rozpoznávánĂ textu k zĂskánĂ oblastĂ, kde se pravdÄ›podobnÄ› vyskytuje text, spolu s ne nutnÄ› správnĂ˝m pĹ™episem. VĂ˝sledkem procesu zahrnujĂcĂho párovánĂ nepĹ™esnĂ˝ch pĹ™episĹŻ se slabĂ˝mi anotacemi a prohledávánĂ okolĂ vedenĂ© Levenshtein vzdálenostĂ jsou skoro bezchybnÄ› lokalizovanĂ© texty, se kterĂ˝mi dále zacházĂme jako s pseudo-anotacemi vyuĹľĂvanĂ˝mi k uÄŤenĂ. AplikovánĂ metody na dva slabÄ› anotovanĂ© datasety a douÄŤenĂ pouĹľitĂ©ho systĂ©mu pomocĂ zĂskanĂ˝ch pseudo-anotacĂ ukazuje, Ĺľe námi navrĹľenĂ˝ proces konzistentnÄ› zlepšuje pĹ™esnost rozpoznávánĂ na rĹŻznĂ˝ch datasetech (jinĂ˝ch domĂ©nách) běžnÄ› vyuĹľĂvanĂ˝ch k testovánĂ a velmi vĂ˝raznÄ› zvyšuje pĹ™esnost na stejnĂ©m datasetu. Metodu lze pouĹľĂt iterativnÄ›.In this work, we present a method for exploiting weakly annotated images to improve text extraction pipelines. The weak annotation of an image is a list of texts that are likely to appear in the image without any information about the location. An arbitrary existing end-to-end text recognition system is used to obtain text region proposals and their, possibly erroneous, transcriptions. A process that includes imprecise transcription to annotation matching and edit distance guided neighbourhood search produces nearly error-free, localised instances of scene text, which we treat as ``pseudo ground truth'' used for training. We apply the method to two weakly-annotated datasets and use the obtained pseudo ground truth to re-train the end-to-end system. The process consistently improves the accuracy of a state of the art recognition model across different benchmark datasets (image domains) as well as providing a significant performance boost on the same dataset, further improving when applied iteratively
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