3,607 research outputs found
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
Automatic tagging and geotagging in video collections and communities
Automatically generated tags and geotags hold great promise
to improve access to video collections and online communi-
ties. We overview three tasks offered in the MediaEval 2010
benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
On the Design and Exploitation of User's Personal and Public Information for Semantic Personal Digital Photograph Annotation
Automating the process of semantic annotation of digital personal photographs is a crucial step towards efficient and effective management of this increasingly high volume of content. However, this is still a highly challenging task for the research community. This paper proposes a novel solution. Our solution integrates all contextual information available to and from the users, such as their daily emails, schedules, chat archives, web browsing histories, documents, online news, Wikipedia data, and so forth. We then analyze this information and extract important semantic terms, using them as semantic keyword suggestions for their photos. Those keywords are in the form of named entities, such as names of people, organizations, locations, and date/time as well as high frequency terms. Experiments conducted with 10 subjects and a total of 313 photos proved that our proposed approach can significantly help users with the annotation process. We achieved a 33% gain in annotation time as compared to manual annotation. We also obtained very positive results in the accuracy rate of our suggested keywords
Creating structure from disorder: using folksonomies to create semantic metadata
This paper reports on an on-going research project to create educational semantic metadata out of folksonomies. The paper describes a simple scenario for the usage of the generated semantic metadata in teaching, and describes the ‘FolksAnnotation’ tool which applies an organization scheme to tags in a specific domain of interest. The contribution of this paper is to describe an evaluation framework which will allow us to validate our claim that folksonomies are potentially a rich source of metadata
Towards Affordable Disclosure of Spoken Word Archives
This paper presents and discusses ongoing work aiming at affordable disclosure of real-world spoken word archives in general, and in particular of a collection of recorded interviews with Dutch survivors of World War II concentration camp Buchenwald. Given such collections, the least we want to be able to provide is search at different levels and a flexible way of presenting results. Strategies for automatic annotation based on speech recognition – supporting e.g., within-document search– are outlined and discussed with respect to the Buchenwald interview collection. In addition, usability aspects of the spoken word search are discussed on the basis of our experiences with the online Buchenwald web portal. It is concluded that, although user feedback is generally fairly positive, automatic annotation performance is still far from satisfactory, and requires additional research
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Gazo bunseki to kanren joho o riyoshita gazo imi rikai ni kansuru kenkyu
制度:新 ; 報告番号:甲3514号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2012/2/8 ; 早大学位記番号:新585
Automatic Metadata Generation using Associative Networks
In spite of its tremendous value, metadata is generally sparse and
incomplete, thereby hampering the effectiveness of digital information
services. Many of the existing mechanisms for the automated creation of
metadata rely primarily on content analysis which can be costly and
inefficient. The automatic metadata generation system proposed in this article
leverages resource relationships generated from existing metadata as a medium
for propagation from metadata-rich to metadata-poor resources. Because of its
independence from content analysis, it can be applied to a wide variety of
resource media types and is shown to be computationally inexpensive. The
proposed method operates through two distinct phases. Occurrence and
co-occurrence algorithms first generate an associative network of repository
resources leveraging existing repository metadata. Second, using the
associative network as a substrate, metadata associated with metadata-rich
resources is propagated to metadata-poor resources by means of a discrete-form
spreading activation algorithm. This article discusses the general framework
for building associative networks, an algorithm for disseminating metadata
through such networks, and the results of an experiment and validation of the
proposed method using a standard bibliographic dataset
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