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Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

Lowe, H. J.; Antipov, I.; Hersh, W.; Smith, C. A.
Fonte: American Medical Informatics Association Publicador: American Medical Informatics Association
Tipo: Artigo de Revista Científica
Publicado em //1998 Português
Relevância na Pesquisa
29.357327%
Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus...

Knowledge assisted data management and retrieval in multimedia database systems

Chen, Min
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
29.845408%
With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. ^ Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular...

A Semantic Medical Multimedia Retrieval Approach Using Ontology Information Hiding

Guo, Kehua; Zhang, Shigeng
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
30.185703%
Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users' query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

Exploiting external knowledge to improve video retrieval

Vallet Weadon, David Jordi; Cantador, Iván; Jose, Joemon M.
Fonte: ACM Publicador: ACM
Tipo: conferenceObject; bookPart
Português
Relevância na Pesquisa
38.626902%
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MIR '10 Proceedings of the international conference on Multimedia information retrieval, http://dx.doi.org/10.1145/1743384.1743406 .; Most video retrieval systems are multimodal, commonly relying on textual information, low- and high-level semantic features extracted from query visual examples. In this work, we study the impact of exploiting different knowledge sources in order to automatically retrieve query visual examples relevant to a video retrieval task. Our hypothesis is that the exploitation of external knowledge sources can help on the identification of query semantics as well as on improving the understanding of video contents. We propose a set of techniques to automatically obtain additional query visual examples from different external knowledge sources, such as DBPedia, Flickr and Google Images, which have different coverage and structure characteristics. The proposed strategies attempt to exploit the semantics underlying the above knowledge sources to reduce the ambiguity of the query, and to focus the scope of the image searches in the repositories. We assess and compare the quality of the images obtained from the different external knowledge sources when used as input of a number of video retrieval tasks. We also study how much they complement manually provided sets of examples...

TagBook: A Semantic Video Representation without Supervision for Event Detection

Mazloom, Masoud; Li, Xirong; Snoek, Cees G. M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 10/10/2015 Português
Relevância na Pesquisa
38.778125%
We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.

Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition

Althoff, Tim; Song, Hyun Oh; Darrell, Trevor
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
38.56355%
While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning required to solve high-level visual tasks such as multimedia event detection and recognition. Recognition or retrieval of events and activities can be improved if specific discriminative objects are detected in a video sequence. In this paper, we propose an image representation, called Detection Bank, based on the detection images from a large number of windowed object detectors where an image is represented by different statistics derived from these detections. This representation is extended to video by aggregating the key frame level image representations through mean and max pooling. We empirically show that it captures complementary information to state-of-the-art representations such as Spatial Pyramid Matching and Object Bank. These descriptors combined with our Detection Bank representation significantly outperforms any of the representations alone on TRECVID MED 2011 data.; Comment: ACM Multimedia 2012

Semantic Computing of Moods Based on Tags in Social Media of Music

Saari, Pasi; Eerola, Tuomas
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/08/2013 Português
Relevância na Pesquisa
38.512273%
Social tags inherent in online music services such as Last.fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling. We validate the technique by predicting listener ratings of moods in music tracks, and compare the results to prediction with the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). The results show that ACT consistently outperforms the baseline techniques, and its performance is robust against a low number of track-level mood tags. The results give validity and analytical insights for harnessing millions of music tracks and associated mood data available through social tags in application development.; Comment: Preprint...

From a Link Semantic to Semantic Links - Building Context in Educational Hypermedia

Schmidt, Thomas C.; Hildebrand, Arne; Engelhardt, Michael; Lange, Dagmar
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/12/2009 Português
Relevância na Pesquisa
38.644397%
Modularization and granulation are key concepts in educational content management, whereas teaching, learning and understanding require a discourse within thematic contexts. Even though hyperlinks and semantically typed references provide the context building blocks of hypermedia systems, elaborate concepts to derive, manage and propagate such relations between content objects are not around at present. Based on Semantic Web standards, this paper makes several contributions to content enrichment. Work starts from harvesting multimedia annotations in class-room recordings, and proceeds to deriving a dense educational semantic net between eLearning Objects decorated with extended LOM relations. Special focus is drawn on the processing of recorded speech and on an Ontological Evaluation Layer that autonomously derives meaningful inter-object relations. Further on, a semantic representation of hyperlinks is developed and elaborated to the concept of semantic link contexts, an approach to manage a coherent rhetoric of linking. These solutions have been implemented in the Hypermedia Learning Objects System (hylOs), our eLearning content management system. hylOs is built upon the more general Media Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE)...

Semantic Modeling and Retrieval of Dance Video Annotations

Kannan, Rajkumar; Ramadoss, Balakrishnan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/01/2010 Português
Relevância na Pesquisa
38.58899%
Dance video is one of the important types of narrative videos with semantic rich content. This paper proposes a new meta model, Dance Video Content Model (DVCM) to represent the expressive semantics of the dance videos at multiple granularity levels. The DVCM is designed based on the concepts such as video, shot, segment, event and object, which are the components of MPEG-7 MDS. This paper introduces a new relationship type called Temporal Semantic Relationship to infer the semantic relationships between the dance video objects. Inverted file based index is created to reduce the search time of the dance queries. The effectiveness of containment queries using precision and recall is depicted. Keywords: Dance Video Annotations, Effectiveness Metrics, Metamodeling, Temporal Semantic Relationships.; Comment: INFOCOMP Journal of Computer Science, Brazil

Image Retrieval And Classification Using Local Feature Vectors

Verma, Vikas
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/09/2014 Português
Relevância na Pesquisa
38.626902%
Content Based Image Retrieval(CBIR) is one of the important subfield in the field of Information Retrieval. The goal of a CBIR algorithm is to retrieve semantically similar images in response to a query image submitted by the end user. CBIR is a hard problem because of the phenomenon known as $\textit {semantic gap}$. In this thesis, we aim at analyzing the performance of a CBIR system build using local feature vectors and Intermediate Matching Kernel. We also propose a Two-Step Matching process for reducing the response time of the CBIR systems. Further, we develop a Meta-Learning framework for improving the retrieval performance of these systems. Our results show that the Two-Step Matching process significantly reduces response time and the Meta-Learning Framework improves the retrieval performance by more than two fold. We also analyze the performance of various image classification systems that use different image representations constructed from the local feature vectors.

Semantic Linking - a Context-Based Approach to Interactivity in Hypermedia

Engelhardt, Michael; Schmidt, Thomas C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/07/2004 Português
Relevância na Pesquisa
38.644397%
The semantic Web initiates new, high level access schemes to online content and applications. One area of superior need for a redefined content exploration is given by on-line educational applications and their concepts of interactivity in the framework of open hypermedia systems. In the present paper we discuss aspects and opportunities of gaining interactivity schemes from semantic notions of components. A transition from standard educational annotation to semantic statements of hyperlinks is discussed. Further on we introduce the concept of semantic link contexts as an approach to manage a coherent rhetoric of linking. A practical implementation is introduced, as well. Our semantic hyperlink implementation is based on the more general Multimedia Information Repository MIR, an open hypermedia system supporting the standards XML, Corba and JNDI.

Hypermedia Learning Objects System - On the Way to a Semantic Educational Web

Engelhardt, Michael; Kárpáti, Andreas; Rack, Torsten; Schmidt, Ivette; Schmidt, Thomas C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/07/2004 Português
Relevância na Pesquisa
38.399026%
While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the present paper we introduce Hylos, an online learning system. Hylos is based on a cellular eLearning Object (ELO) information model encapsulating meta data conforming to the LOM standard. Content management is provisioned on this semantic meta data level and allows for variable, dynamically adaptable access structures. Context aware multifunctional links permit a systematic navigation depending on the learners and didactic needs, thereby exploring the capabilities of the semantic web. Hylos is built upon the more general Multimedia Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE), its linking extension. MIR is an open system supporting the standards XML, Corba and JNDI. Hylos benefits from manageable information structures, sophisticated access logic and high-level authoring tools like the ELO editor responsible for the semi-manual creation of meta data and WYSIWYG like content editing.; Comment: 11 pages...

Sentence based semantic similarity measure for blog-posts

Aziz, Mehwish; Rafi, Muhammad
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 10/01/2012 Português
Relevância na Pesquisa
38.215%
Blogs-Online digital diary like application on web 2.0 has opened new and easy way to voice opinion, thoughts, and like-dislike of every Internet user to the World. Blogosphere has no doubt the largest user-generated content repository full of knowledge. The potential of this knowledge is still to be explored. Knowledge discovery from this new genre is quite difficult and challenging as it is totally different from other popular genre of web-applications like World Wide Web (WWW). Blog-posts unlike web documents are small in size, thus lack in context and contain relaxed grammatical structures. Hence, standard text similarity measure fails to provide good results. In this paper, specialized requirements for comparing a pair of blog-posts is thoroughly investigated. Based on this we proposed a novel algorithm for sentence oriented semantic similarity measure of a pair of blog-posts. We applied this algorithm on a subset of political blogosphere of Pakistan, to cluster the blogs on different issues of political realm and to identify the influential bloggers.; Comment: 6th International Conference on Digital Content, Multimedia Technology and its Applications (IDC), 2010

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Li, Xirong; Uricchio, Tiberio; Ballan, Lamberto; Bertini, Marco; Snoek, Cees G. M.; Del Bimbo, Alberto
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
38.849065%
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.

An interactive engine for multilingual video browsing using semantic content

Halima, M. Ben; Hamroun, M.; Moussa, S. Ben; Alimi, A. M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/08/2013 Português
Relevância na Pesquisa
38.207126%
The amount of audio-visual information has increased dramatically with the advent of High Speed Internet. Furthermore, technological advances in recent years in the field of information technology, have simplified the use of video data in various fields by the general public. This made it possible to store large collections of video documents into computer systems. To enable efficient use of these collections, it is necessary to develop tools to facilitate access to these documents and handling them. In this paper we propose a method for indexing and retrieval of video sequences in a video database of large dimension, based on a weighting technique to calculate the degree of membership of a concept in a video also a structuring of the data of the audio-visual (context / concept / video) and a relevance feedback mechanism.; Comment: 4 pages, IGS 2013 Conference; IGS 2013

Video Data Visualization System: Semantic Classification And Personalization

Slimi, Jamel; Ammar, Anis Ben; Alimi, Adel M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/09/2012 Português
Relevância na Pesquisa
38.512273%
We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.; Comment: graphics

Realization of Semantic Atom Blog

Patel, Dhiren R.; Khuba, Sidheshwar A.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/12/2009 Português
Relevância na Pesquisa
38.778125%
Web blog is used as a collaborative platform to publish and share information. The information accumulated in the blog intrinsically contains the knowledge. The knowledge shared by the community of people has intangible value proposition. The blog is viewed as a multimedia information resource available on the Internet. In a blog, information in the form of text, image, audio and video builds up exponentially. The multimedia information contained in an Atom blog does not have the capability, which is required by the software processes so that Atom blog content can be accessed, processed and reused over the Internet. This shortcoming is addressed by exploring OWL knowledge modeling, semantic annotation and semantic categorization techniques in an Atom blog sphere. By adopting these techniques, futuristic Atom blogs can be created and deployed over the Internet.

Social Recommendations within the Multimedia Sharing Systems

Musial, Katarzyna; Kazienkol, Przemyslaw; Kajdanowicz, Tomasz
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/03/2013 Português
Relevância na Pesquisa
38.44135%
The social recommender system that supports the creation of new relations between users in the multimedia sharing system is presented in the paper. To generate suggestions the new concept of the multirelational social network was introduced. It covers both direct as well as object-based relationships that reflect social and semantic links between users. The main goal of the new method is to create the personalized suggestions that are continuously adapted to users' needs depending on the personal weights assigned to each layer from the social network. The conducted experiments confirmed the usefulness of the proposed model.; Comment: recommender system, multirelational social network, multimedia sharing system, social network analysis, Best Paper Award. arXiv admin note: text overlap with arXiv:1303.0093

Exploiting multimedia content : a machine learning based approach

Hassa, Ehtesham
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf; application/pdf
Publicado em //2014 Português
Relevância na Pesquisa
29.780884%
Advisors: Prof. M Gopal, Prof. Santanu Chaudhury. Date and location of PhD thesis defense: 10 September 2013, Indian Institute of Technology Delhi; This thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page...

Accelerating semantic search with application of specific platforms

Montón i Macián, Màrius; Carrabina, Jordi; Montero, Carlos; Serrano, Javier; Binefa i Valls, Xavier; Gracia, Ciro; Blázquez, Mercedes; Contreras, Jesús; Teodoro, Emma; Casellas, Núria; Vallbé, Joan Josep; Poblet, Marta; Casanovas, Pompeu
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Conferência ou Objeto de Conferência Formato: application/pdf
Publicado em //2007 Português
Relevância na Pesquisa
38.399026%
Semantic Search and Ontologies are one of the key technologies that can improve content management. Nonetheless, in order to be widely diffused, these technologies lack real-time capabilities, that speed up both the indexing and the retrieval processes. This contribution presents the approach and strategy proposed to tackle this problem, within the Spanish project E-Sentencias; a project for the development of a management system for lawyers that includes documentation and multimedia related to the management of their legal cases