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1 edition of DEMON- a media object model incorporating natural language descriptions for retrieval support found in the catalog.

DEMON- a media object model incorporating natural language descriptions for retrieval support

Bernhard Holtkamp

DEMON- a media object model incorporating natural language descriptions for retrieval support

by Bernhard Holtkamp

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  • 1 Currently reading

Published by Naval Postgraduate School in Monterey, California .
Written in English

    Subjects:
  • Object-oriented databases,
  • Databases

  • About the Edition

    The DEMOM (Description based Media Object data Model) media object data model aims at providing a uniform framework for managing different types of media data, i.e., images, text, sound or graphics. According to DEMOM media objects are defined as a class hierarchy of objects, i.e., images, text, sound, and graphics being subtypes of the general type media object. Representation specific objects are regarded as subordinate types of the corresponding subtype, e.g. a SUN raster image in pixrect format is an instance of the subtype pixrect which is in turn a subtype of image. Using images as an example we discuss the media object hierarchy, the corresponding access operations and implementation issues. Content oriented search of media data on the basis of predicate calculus is considered as an essential part of DEMOM and hence discussed as well.

    Edition Notes

    Statement[by] Bernhard Holtkamp, Vincent Y. Lum [and] Neil C. Rowe
    ContributionsLum, Vincent Y., Rowe, Neil C., Naval Postgraduate School (U.S.)
    The Physical Object
    Pagination20 p. :
    Number of Pages20
    ID Numbers
    Open LibraryOL25515209M
    OCLC/WorldCa471775848

    bilistic language model that captures the compositional and hierarchical structure of natural language descriptions. Ad-ditionally, our method maintains a distribution over a sparse, structured model of an object’s kinematics, which provides a common representation with which to fuse disparate lin-guistic and visual observations. The model has achieved very good retrieval results. Compared to other probabilistic approaches, such as the BIM from Chap the main difference initially appears to be that the LM approach does away with explicitly modeling relevance (whereas this is the central variable evaluated in the BIM approach).

    InterestMap, A Cultural Fabric of Identities & Interests Hugo Liu and Pattie Maes () Over recent years, social network communities (e.g. inter alia, friendster, contact lists, weblog communities, newsgroups) have been steadily building up in the online is now sufficient critical mass of such infrastructure to postulate things about identity and the Self, as reflected in the. Link-builder object j builds, or defines, hyperlinks, such as hyperlinks a, b, and c, according a URL standard in accord with the particularly, once content-finder object c creates a found object c, link-builder object defines a URL for the URL in the exemplary embodiment includes a domain name and at least a portion of the text with which it.

    Doc2vec Github - Doc2vec Github. Using natural language analysis. Pioneered by Russell Abbott (), popularized by Grady Booch; Not perfect, but coupled with other techniques, it's a good start; This can be done from a general problem description, or better, from a use case or scenario; Map parts of speech to object model components. nouns usually map to classes, objects, or.


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DEMON- a media object model incorporating natural language descriptions for retrieval support by Bernhard Holtkamp Download PDF EPUB FB2

DEMON - A Media Object Model Incorporating Natural Language Descriptions for Retrieval Support Article (PDF Available) February with 15 Reads How we measure 'reads'. DEMON - A Media Object Model Incorporating Natural Language Descriptions for Retrieval Support Vincent Y.

Lum. Neil Rowe. The DEMOM (Description based Media Object data Model) media object. Hence, partial or approximate match between descriptions of multimedia data and user queries is generally required during multimedia data retrieval.

We propose an intelligent approach to approximate match by integrating both object-oriented and natural language understanding techniques. In order to make the query specification process easier we Cited by: 6. 3 Natural language processing.

Our NLP approach extracts models from the textual descriptions obtained from eNature (Figure1). Although full natural language understanding (such as full summarisation of hu-{Farhadi, Endres, Hoiem, and Forsyth} {Lampert, Nickisch, and Harmeling} {Farhadi, Endres, Hoiem, and Forsyth} Graphical User Interface Multimedia Database System Natural-Language Interface Information Retrieval This research was mainly done while the authors were at the Computer Science Department, Naval Postgraduate School, Monterey, California, USA and was supported in part by NOSC., Direct Funding and the German Scholarship by: The book starts with a general description of the monolingual IR and CLIR problems.

and even eye movements combine to support a more natural user interface (NUI). (Information Retrieval. Overview. A modeling language can be graphical or textual. Graphical modeling languages use a diagram technique with named symbols that represent concepts and lines that connect the symbols and represent relationships and various other graphical notation to represent constraints.; Textual modeling languages may use standardized keywords accompanied by parameters or natural language terms.

We present a model that generates free-form natural language descriptions of image regions. class as the input object image. Each 3D model is represented by a set of views, and we study the.

To incorporate language context, we construct a language scene graph from the description (e.g., Schuster et al. ; Wang et al. b) in which the nodes are noun phrases, and the edges encode. Economics of retrieval There has been much progress over the years in modeling information retrieval systems.

There has been work done in extending the standard models, work done in incorporating artificial intelligence and natural language processing, and work done on improving the human-computer interface for retrieval systems.

Natural language processing. Concept extraction. The following are brief descriptions of the problems faced in each practice area, a guide to the resources available in this book, and references to other resources if you wish to delve deeper into any of the areas.

Some of these attempts have resulted in concept models that support structured data entry and image retrieval, providing a model for analyzing sets of natural-language reports, 9, 10 although such efforts have typically not been based on industry-supported standards such as DICOM.

A one‐way ANOVA test shows statistically significant differences in the use of memory, LT provided descriptions, LT provided book information, and LT tag cloud as reference sources by participants with different familiarities with the books (F‐values are,and respectively; the p‐value are all less than ).

Bates, Marcia J. Concepts for the study of information embodiment. Library Trends, 66(3): The growing study in information science of the role of the body in human information practice may benefit from the concepts developed around a set of fundamental forms of information previously published by the author.

The crucial step in the retrieval module is similarity comparison which estimates the similarity of different feature-based media descriptions. Similarity judgments usually base on distance measurements.

The most popular approach in this context is the vector space model [5]. The basic assumption of this model is that the numeric values of a. In part 4 of our "Cruising the Data Ocean" blog series, Chief Architect, Paul Nelson, provides a deep-dive into Natural Language Processing (NLP) tools and techniques that can be used to extract insights from unstructured or semi-structured content written in natural languages.

Paul will introduce six essential steps (with specific examples) for a successful NLP project. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions.

The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. Int J Comput Vis () – Fig. 1 Illustrative example to convey the role of relative importance in image retrieval.

The left image is a query, and the two rows on the right are two possible sets of retrieval results. Both rows share similar num-bers of total objects with the query. Text analytics. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.

The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a description of "text mining" in to. A category tree consists of a set of categories selected from the category space and the subcategory relations, represented as CT=({C 1 (p 1),C n (p n)}, ≤), where ≤ denotes the subcategory relation between categories, and the pattern of a category is the generalization of the patterns of its children, reflecting the common the category tree, it is not difficult.

A system and method for representing, storing and retrieving real-world knowledge on a computer or network of computers is disclosed.

Knowledge is broken down into permanent atomic “facts” which can be stored in a standard relational database and processed very efficiently. It also provides for the efficient querying of a knowledge base, efficient inference of new knowledge and translation.

The vision deep representations from the original visual ia deep convolutional neural network. The am utilizes the natural language descriptions oint out the discriminative parts or charac- ach image, and provides a flexible and com- Heermann Gull Herring Gull Slaty-backed Gull Western Gullinter-class variance intra-classvariance Figure 1.A data model, called the entity-relationship model, is proposed.

This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design.

An example of database design and description using the model and the diagrammatic technique is given.