16,712 research outputs found
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
Using association rule mining to enrich semantic concepts for video retrieval
In order to achieve true content-based information retrieval on video we should analyse and index video with
high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date,
etc. However the range of such high-level semantic concepts, detected either manually or automatically,
usually limited compared to the richness of information content in video and the potential vocabulary of
available concepts for indexing. Even though there is work to improve the performance of individual concept
classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences
are available to us. We describe in this paper our method for using association rule mining to automatically
enrich the representation of video content through a set of semantic concepts based on concept co-occurrence
patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts
of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach
A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages
Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot.Comment: 8 pages, 7 figures, 3 table
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
The task of a visual landmark recognition system is to identify photographed
buildings or objects in query photos and to provide the user with relevant
information on them. With their increasing coverage of the world's landmark
buildings and objects, Internet photo collections are now being used as a
source for building such systems in a fully automatic fashion. This process
typically consists of three steps: clustering large amounts of images by the
objects they depict; determining object names from user-provided tags; and
building a robust, compact, and efficient recognition index. To this date,
however, there is little empirical information on how well current approaches
for those steps perform in a large-scale open-set mining and recognition task.
Furthermore, there is little empirical information on how recognition
performance varies for different types of landmark objects and where there is
still potential for improvement. With this paper, we intend to fill these gaps.
Using a dataset of 500k images from Paris, we analyze each component of the
landmark recognition pipeline in order to answer the following questions: How
many and what kinds of objects can be discovered automatically? How can we best
use the resulting image clusters to recognize the object in a query? How can
the object be efficiently represented in memory for recognition? How reliably
can semantic information be extracted? And finally: What are the limiting
factors in the resulting pipeline from query to semantics? We evaluate how
different choices of methods and parameters for the individual pipeline steps
affect overall system performance and examine their effects for different query
categories such as buildings, paintings or sculptures
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