304 research outputs found
COST292 experimental framework for TRECVID 2006
In this paper we give an overview of the four TRECVID tasks submitted by COST292, European network of institutions in the area of semantic multimodal analysis and retrieval of digital video media. Initially, we present shot boundary evaluation method based on results merged using a confidence measure. The two SB detectors user here are presented, one of the Technical University of Delft and one of the LaBRI, University of Bordeaux 1, followed by the description of the merging algorithm. The high-level feature extraction task comprises three separate systems. The first system, developed by the National Technical University of Athens (NTUA) utilises a set of MPEG-7 low-level descriptors and Latent Semantic Analysis to detect the features. The second system, developed by Bilkent University, uses a Bayesian classifier trained with a "bag of subregions" for each keyframe. The third system by the Middle East Technical University (METU) exploits textual information in the video using character recognition methodology. The system submitted to the search task is an interactive retrieval application developed by Queen Mary, University of London, University of Zilina and ITI from Thessaloniki, combining basic retrieval functionalities in various modalities (i.e. visual, audio, textual) with a user interface supporting the submission of queries using any combination of the available retrieval tools and the accumulation of relevant retrieval results over all queries submitted by a single user during a specified time interval. Finally, the rushes task submission comprises a video summarisation and browsing system specifically designed to intuitively and efficiently presents rushes material in video production environment. This system is a result of joint work of University of Bristol, Technical University of Delft and LaBRI, University of Bordeaux 1
The use of images and descriptive words for the development of an image database for product designers
This research aims to understand the role images currently play within the design process, in order to develop a classification of image types and reference keywords to construct an electronic image database for professional use in product design.
Images play an important role in the design process, both in defining the context for designs and in informing the creation of individual design. They are also used to communicate with clients, to understand consumers, to assist in expressing the themes of the project, to understand the related environments, or to search for inspiration or functional solutions. Designers usually have their own collections of images, however for each project they still spend a significant amount of time searching images, either looking within their own collection or searching for new images. This study is based on the assumption that there is a structure that can show the relationship between the image itself and the information it conveys and can be used to develop the database. A product-image database will enable designers to consult images more easily and this will also facilitate communication of visual ideas among designers or between designers and their clients, thus augmenting its potential value in the professional design process. Also, the value of an image may be enhanced by applying its linguistic associations through descriptions and keywords which identify and interpret its content.
Through a series of interviews, workshops, and understanding relevant issues, such as design method, linguistic theory, perception psychology and so on, a prototype database system was developed. It was developed based on three information divisions: SPECIFICATION, CHARACTERISTIC, and EMOTION. The three divisions construct a model of the information which an image conveys.
The database prototype was tested and evaluated by groups of students and professional designers. The results showed that users understand the concept and working of the database and appreciated its value. They also indicated that the CHARACTERISTIC division was most valuable as it allows users to record images through their recollection of feelings
A Comparative Emotions-detection Review for Non-intrusive Vision-Based Facial Expression Recognition
Affective computing advocates for the development of systems and devices that can recognize, interpret, process, and simulate human emotion. In computing, the field seeks to enhance the user experience by finding less intrusive automated solutions. However, initiatives in this area focus on solitary emotions that limit the scalability of the approaches. Further reviews conducted in this area have also focused on solitary emotions, presenting challenges to future researchers when adopting these recommendations. This review aims at highlighting gaps in the application areas of Facial Expression Recognition Techniques by conducting a comparative analysis of various emotion detection datasets, algorithms, and results provided in existing studies. The systematic review adopted the PRISMA model and analyzed eighty-three publications. Findings from the review show that different emotions call for different Facial Expression Recognition techniques, which should be analyzed when conducting Facial Expression Recognition.
Keywords: Facial Expression Recognition, Emotion Detection, Image Processing, Computer Visio
Natural Language Processing in-and-for Design Research
We review the scholarly contributions that utilise Natural Language
Processing (NLP) methods to support the design process. Using a heuristic
approach, we collected 223 articles published in 32 journals and within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research
Content And Multimedia Database Management Systems
A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the âdatabase approachâ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objectsâ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems
Semantic multimedia modelling & interpretation for search & retrieval
With the axiomatic revolutionary in the multimedia equip devices, culminated in the proverbial proliferation of the image and video data. Owing to this omnipresence and progression, these data become the part of our daily life. This devastating data production rate accompanies with a predicament of surpassing our potentials for acquiring this data. Perhaps one of the utmost prevailing problems of this digital era is an information plethora.
Until now, progressions in image and video retrieval research reached restrained success owed to its interpretation of an image and video in terms of primitive features. Humans generally access multimedia assets in terms of semantic concepts. The retrieval of digital images and videos is impeded by the semantic gap. The semantic gap is the discrepancy between a userâs high-level interpretation of an image and the information that can be extracted from an imageâs physical properties. Content- based image and video retrieval systems are explicitly assailable to the semantic gap due to their dependence on low-level visual features for describing image and content. The semantic gap can be narrowed by including high-level features. High-level descriptions of images and videos are more proficient of apprehending the semantic meaning of image and video content.
It is generally understood that the problem of image and video retrieval is still far from being solved. This thesis proposes an approach for intelligent multimedia semantic extraction for search and retrieval. This thesis intends to bridge the gap between the visual features and semantics. This thesis proposes a Semantic query Interpreter for the images and the videos. The proposed Semantic Query Interpreter will select the pertinent terms from the user query and analyse it lexically and semantically. The proposed SQI reduces the semantic as well as the vocabulary gap between the users and the machine. This thesis also explored a novel ranking strategy for image search and retrieval. SemRank is the novel system that will incorporate the Semantic Intensity (SI) in exploring the semantic relevancy between the user query and the available data. The novel Semantic Intensity captures the concept dominancy factor of an image. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other. The SemRank will rank the retrieved images on the basis of Semantic Intensity.
The investigations are made on the LabelMe image and LabelMe video dataset. Experiments show that the proposed approach is successful in bridging the semantic gap. The experiments reveal that our proposed system outperforms the traditional image retrieval systems
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und QualitÀt der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien fĂŒr die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten ĂŒberprĂŒft.
Heutzutage gehören Evaluation, Test und QualitÀtsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunÀchst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme ĂŒbertragen. Dies geschah hĂ€ufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die verÀnderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschÀftigt
sich mit der system-basierten Evaluation von Indizierungssystemen fĂŒr
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen fĂŒr das Suchen und Verschlagworten von
Bildern, EvaluationsmaĂe fĂŒr die QualitĂ€tsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlÀssigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und EvaluationsmaĂe zur QualitĂ€tsbewertung eingefĂŒhrt, analysiert und
experimentell verglichen. Traditionelle MaĂe zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. EvaluationsmaĂe vergeben
ĂŒblicherweise binĂ€re Kosten fĂŒr korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
ĂŒberprĂŒft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ăhnlichkeiten visueller Konzepte automatisiert abgeschĂ€tzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell fĂŒr die konzeptbasierte Suche von Bildern,
eine vollstĂ€ndig bewertete Testkollektion und neue EvaluationsmaĂe fĂŒr die
anforderungsgerechte QualitÀtsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und QualitÀt der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien fĂŒr die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten ĂŒberprĂŒft.
Heutzutage gehören Evaluation, Test und QualitÀtsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunÀchst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme ĂŒbertragen. Dies geschah hĂ€ufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die verÀnderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschÀftigt
sich mit der system-basierten Evaluation von Indizierungssystemen fĂŒr
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen fĂŒr das Suchen und Verschlagworten von
Bildern, EvaluationsmaĂe fĂŒr die QualitĂ€tsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlÀssigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und EvaluationsmaĂe zur QualitĂ€tsbewertung eingefĂŒhrt, analysiert und
experimentell verglichen. Traditionelle MaĂe zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. EvaluationsmaĂe vergeben
ĂŒblicherweise binĂ€re Kosten fĂŒr korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
ĂŒberprĂŒft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ăhnlichkeiten visueller Konzepte automatisiert abgeschĂ€tzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell fĂŒr die konzeptbasierte Suche von Bildern,
eine vollstĂ€ndig bewertete Testkollektion und neue EvaluationsmaĂe fĂŒr die
anforderungsgerechte QualitÀtsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
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