11,974 research outputs found
Hierarchical structuring of Cultural Heritage objects within large aggregations
Huge amounts of cultural content have been digitised and are available
through digital libraries and aggregators like Europeana.eu. However, it is not
easy for a user to have an overall picture of what is available nor to find
related objects. We propose a method for hier- archically structuring cultural
objects at different similarity levels. We describe a fast, scalable clustering
algorithm with an automated field selection method for finding semantic
clusters. We report a qualitative evaluation on the cluster categories based on
records from the UK and a quantitative one on the results from the complete
Europeana dataset.Comment: The paper has been published in the proceedings of the TPDL
conference, see http://tpdl2013.info. For the final version see
http://link.springer.com/chapter/10.1007%2F978-3-642-40501-3_2
FAME: Face Association through Model Evolution
We attack the problem of learning face models for public faces from
weakly-labelled images collected from web through querying a name. The data is
very noisy even after face detection, with several irrelevant faces
corresponding to other people. We propose a novel method, Face Association
through Model Evolution (FAME), that is able to prune the data in an iterative
way, for the face models associated to a name to evolve. The idea is based on
capturing discriminativeness and representativeness of each instance and
eliminating the outliers. The final models are used to classify faces on novel
datasets with possibly different characteristics. On benchmark datasets, our
results are comparable to or better than state-of-the-art studies for the task
of face identification.Comment: Draft version of the stud
Evolution of a Web-Scale Near Duplicate Image Detection System
Detecting near duplicate images is fundamental to the content ecosystem of
photo sharing web applications. However, such a task is challenging when
involving a web-scale image corpus containing billions of images. In this
paper, we present an efficient system for detecting near duplicate images
across 8 billion images. Our system consists of three stages: candidate
generation, candidate selection, and clustering. We also demonstrate that this
system can be used to greatly improve the quality of recommendations and search
results across a number of real-world applications.
In addition, we include the evolution of the system over the course of six
years, bringing out experiences and lessons on how new systems are designed to
accommodate organic content growth as well as the latest technology. Finally,
we are releasing a human-labeled dataset of ~53,000 pairs of images introduced
in this paper
An Efficient Approach for Finding Near Duplicate Web pages using Minimum Weight Overlapping Method
The existence of billions of web data has severely affected the performance and reliability of web search. The presence of near duplicate web pages plays an important role in this performance degradation while integrating data from heterogeneous sources. Web mining faces huge problems due to the existence of such documents. These pages increase the index storage space and thereby increase the serving cost. By introducing efficient methods to detect and remove such documents from the Web not only decreases the computation time but also increases the relevancy of search results. We aim a novel idea for finding near duplicate web pages which can be incorporated in the field of plagiarism detection, spam detection and focused web crawling scenarios. Here we propose an efficient method for finding near duplicates of an input web page, from a huge repository. A TDW matrix based algorithm is proposed with three phases, rendering, filtering and verification, which receives an input web page and a threshold in its first phase, prefix filtering and positional filtering to reduce the size of record set in the second phase and returns an optimal set of near duplicate web pages in the verification phase by using Minimum Weight Overlapping (MWO) method. The experimental results show that our algorithm outperforms in terms of two benchmark measures, precision and recall, and a reduction in the size of competing record set.DOI:http://dx.doi.org/10.11591/ijece.v1i2.7
Network Archaeology: Uncovering Ancient Networks from Present-day Interactions
Often questions arise about old or extinct networks. What proteins interacted
in a long-extinct ancestor species of yeast? Who were the central players in
the Last.fm social network 3 years ago? Our ability to answer such questions
has been limited by the unavailability of past versions of networks. To
overcome these limitations, we propose several algorithms for reconstructing a
network's history of growth given only the network as it exists today and a
generative model by which the network is believed to have evolved. Our
likelihood-based method finds a probable previous state of the network by
reversing the forward growth model. This approach retains node identities so
that the history of individual nodes can be tracked. We apply these algorithms
to uncover older, non-extant biological and social networks believed to have
grown via several models, including duplication-mutation with complementarity,
forest fire, and preferential attachment. Through experiments on both synthetic
and real-world data, we find that our algorithms can estimate node arrival
times, identify anchor nodes from which new nodes copy links, and can reveal
significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure
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