Video Ontology

2009 ◽  
pp. 1506-1511
Author(s):  
Jeongkyu Lee
Keyword(s):  
Language Processing ◽  
Building Blocks ◽  
Automatic Generation ◽  
Domain Ontology ◽  
Multimedia Data ◽  
Multimedia Content ◽  
Surveillance Systems ◽  
Low Level ◽  
Core Ontology ◽  
Feature Values

There has been a great deal of interest in the development of ontology to facilitate knowledge sharing and database integration. In general, ontology is a set of terms or vocabularies of interest in a particular information domain, and shows the relationships among them (Doerr, Hunter, & Lagoze, 2003). It includes machine-interpretable definitions of basic concepts in the domain. Ontology is very popular in the fields of natural language processing (NLP) and Web user interface (Web ontology). To take this advantage into multimedia content analysis, several studies have proposed ontology-based schemes (Hollink & Worring, 2005; Spyropoulos, Paliouras, Karkaletsis, Kosmopoulos, Pratikakis, Perantonis, & Gatos, 2005). Modular structure of the ontology methodology is used in a generic analysis scheme to semantically interpret and annotate multimedia content. This methodology consists of domain ontology, core ontology, and multimedia ontology. Domain ontology captures concepts in a particular type of domain, while core ontology is the key building blocks necessary to enable the scalable assimilation of information from diverse sources. Multimedia ontology is used to model multimedia data, such as audio, image, and video. In the multimedia data analysis the meaningful patterns and hidden knowledge are discovered from the database. There are existing tools for managing and searching the discovered patterns and knowledge. However, almost all of the approaches use low-level feature values instead of high-level perceptions, which make a huge gap between machine interpretation and human understanding. For example, if we have to retrieve anomaly from video surveillance systems, low-level feature values cannot represent such semantic meanings. In order to address the problem, the main focus of research has been on the construction and utilization of ontology for specific data domain in various applications. In this chapter, we first survey the state-of-the-art in multimedia ontology, specifically video ontology, and then investigate the methods of automatic generation of video ontology.

scholarly journals Manipuri Morphological Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 4-10
Author(s):  
Y. Bablu Singh ◽  
Th. Mamata Devi ◽  
Ch. Yashawanta Singh
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Morphological Analysis ◽  
Building Blocks ◽  
Automatic Indexing ◽  
Standard Format ◽  
Computational Morphology ◽  
Word Forms ◽  
Computer Based ◽  
Feature Values

Morphological analysis is the basic foundation in Natural Language Processing applications including Syntax Parsing, Machine Translation (MT), Information Retrieval (IR) and Automatic Indexing. Morphological Analysis can provide valuable information for computer based linguistics task such as Lemmatization and studies of internal structure of the words or the feature values of the word. Computational Morphology is the application of morphological rules in the field of Computational Linguistics, and it is the emerging area in AI, which studies the structure of words, which are formed by combining smaller units of linguistics information, called morphemes: the building blocks of words. It provides about Semantic and Syntactic role in a sentence. It can analyze the Manipuri word forms and produces grammatical information, which is associated with the lexicon. Morphological Analyzer for Manipuri language has been tested on 4500 Manipuri lexicons in Shakti Standard Format (SSF) using Meitei Mayek Unicode as source; thereby an accuracy of 84% has been obtained on a manual check.

METHOD OF DOMAIN ONTOLOGY AUTOMATED REPLENISHMENT FOR THE SUPPORT OF NEW TECHNICAL SOLUTIONS SYNTHESIS. PART I

2021 ◽  
pp. 3-12
Author(s):  
S. S. Vasiliev ◽  
D. M. Korobkin ◽  
S. A. Fomenkov
Keyword(s):  
Language Processing ◽  
Domain Ontology ◽  
Russian Language ◽  
Information Support ◽  
Structural Elements ◽  
Technical Object ◽  
Main Claim ◽  
Shallow Parsing ◽  
Technical Solutions ◽  
Alternative Designs

To solve the problem of information support for the synthesis of new technical solutions, a method of extracting structured data from an array of Russian-language patents is presented. The key features of the invention, such as the structural elements of the technical object and the relationships between them, are considered as information support. The data source addresses the main claim of the invention in the device patent. The unit of extraction is the semantic structure Subject-Action-Object (SAO), which semantically describes the constructive elements. The extraction method is based on shallow parsing and claim segmentation, taking into account the specifics of writing patent texts. Often the excessive length of the claim sentence and the specificity of the patent language make it difficult to efficiently use off-the-shelf tools for data extracting. All processing steps include: segmentation of the claim sentences; extraction of primary SAO structures; construction of the graph of the construct elements f the invention; integration of the data into the domain ontology. This article deals with the first two stages. Segmentation is carried out according to a number of heuristic rules, and several natural language processing tools are used to reduce analysis errors. The primary SAO elements are extracted considering the valences of the predefined semantic group of verbs, as well as information about the type of processed segment. The result of the work is the organization of the domain ontology, which can be used to find alternative designs for nodes in a technical object. In the second part of the article, an algorithm for constructing a graph of structural elements of a separate technical object, an assessment of the effectiveness of the system, as well as ontology organization and the result are considered.

scholarly journals Hemispheric and executive influences on low-level language processing after traumatic brain injury

Brain Injury ◽  
2012 ◽  
Vol 26 (7-8) ◽  
pp. 984-995 ◽  
Author(s):  
Kathryn C. Russell ◽  
Patricia M. Arenth ◽  
Joelle M. Scanlon ◽  
Lauren Kessler ◽  
Joseph H. Ricker
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Language Processing ◽  
Low Level

scholarly journals Ultrastructural Features of Membranous Replication Organelles Induced by Positive-Stranded RNA Viruses

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2407
Author(s):  
Van Nguyen-Dinh ◽  
Eva Herker
Keyword(s):  
Rna Viruses ◽  
Scientific Progress ◽  
Membrane System ◽  
Building Blocks ◽  
Surveillance Systems ◽  
Animal Cells ◽  
Cell Organelles ◽  
Positive Strand ◽  
Successful Infection ◽  
Positive Strand Rna

All intracellular pathogens critically depend on host cell organelles and metabolites for successful infection and replication. One hallmark of positive-strand RNA viruses is to induce alterations of the (endo)membrane system in order to shield their double-stranded RNA replication intermediates from detection by the host cell’s surveillance systems. This spatial seclusion also allows for accruing host and viral factors and building blocks required for efficient replication of the genome and prevents access of antiviral effectors. Even though the principle is iterated by almost all positive-strand RNA viruses infecting plants and animals, the specific structure and the organellar source of membranes differs. Here, we discuss the characteristic ultrastructural features of the virus-induced membranous replication organelles in plant and animal cells and the scientific progress gained by advanced microscopy methods.

scholarly journals Automatically Representing TExt Meaning via an Interlingua-based System (ARTEMIS). A further step towards the computational representation of RRG

2017 ◽  
Vol 1 (1) ◽  
pp. 61 ◽  
Author(s):  
Ricardo Mairal-Usón ◽  
Francisco Cortés-Rodríguez
Keyword(s):  
Language Processing ◽  
Semantic Representation ◽  
Analysis Data ◽  
Logical Structure ◽  
Automatic Generation ◽  
Role And Reference Grammar ◽  
Reference Grammar ◽  
Level 1 ◽  
Computational Resources ◽  
Computational Representation

Within the framework of FUNK Lab – a virtual laboratory for natural language processing inspired on a functionally-oriented linguistic theory like Role and Reference Grammar-, a number of computational resources have been built dealing with different aspects of language and with an application in different scientific domains, i.e. terminology, lexicography, sentiment analysis, document classification, text analysis, data mining etc. One of these resources is ARTEMIS (<span style="text-decoration: underline;">A</span>utomatically <span style="text-decoration: underline;">R</span>epresenting <span style="text-decoration: underline;">TE</span>xt <span style="text-decoration: underline;">M</span>eaning via an <span style="text-decoration: underline;">I</span>nterlingua-Based <span style="text-decoration: underline;">S</span>ystem), which departs from the pioneering work of Periñán-Pascual (2013) and Periñán-Pascual &amp; Arcas (2014).  This computational tool is a proof of concept prototype which allows the automatic generation of a conceptual logical structure (CLS) (cf. Mairal-Usón, Periñán-Pascual and Pérez 2012; Van Valin and Mairal-Usón 2014), that is, a fully specified semantic representation of an input text on the basis of a reduced sample of sentences. The primary aim of this paper is to develop the syntactic rules that form part of the computational grammar for the representation of simple clauses in English. More specifically, this work focuses on the format of those syntactic rules that account for the upper levels of the RRG Layered Structure of the Clause (LSC), that is, the <em>core</em> (and the level-1 construction associated with it), the <em>clause</em> and the <em>sentence </em>(Van Valin 2005). In essence, this analysis, together with that in Cortés-Rodríguez and Mairal-Usón (2016), offers an almost complete description of the computational grammar behind the LSC for simple clauses.

scholarly journals Incrementality and intention-recognition in utterance processing

2011 ◽  
Vol 2 (1) ◽  
pp. 199-233 ◽  
Author(s):  
Eleni Gregoromichelaki ◽  
Ruth Kempson ◽  
Matthew Purver ◽  
Gregory J. Mills ◽  
Ronnie Cann ◽  
...  
Keyword(s):  
Predictive Model ◽  
Language Processing ◽  
Mental States ◽  
Higher Order ◽  
Low Level ◽  
Intention Recognition ◽  
Dialogue Modelling ◽  
Open Question ◽  
Dynamic Syntax ◽  
Do So

Ever since dialogue modelling first developed relative to broadly Gricean assumptions about utter-ance interpretation (Clark, 1996), it has remained an open question whether the full complexity of higher-order intention computation is made use of in everyday conversation. In this paper we examine the phenomenon of split utterances, from the perspective of Dynamic Syntax, to further probe the necessity of full intention recognition/formation in communication: we do so by exploring the extent to which the interactive coordination of dialogue exchange can be seen as emergent from low-level mechanisms of language processing, without needing representation by interlocutors of each other’s mental states, or fully developed intentions as regards messages to be conveyed. We thus illustrate how many dialogue phenomena can be seen as direct consequences of the grammar architecture, as long as this is presented within an incremental, goal-directed/predictive model.

Event Detection in Sports Video Based on Generative-Discriminative Models

2011 ◽  
pp. 143-165
Author(s):  
Guoliang Fan ◽  
Yi Ding
Keyword(s):  
Event Detection ◽  
Semantic Analysis ◽  
Building Blocks ◽  
Semantic Space ◽  
Sports Video ◽  
Low Level ◽  
Discriminative Models ◽  
Video Mining ◽  
High Level ◽  
Different Levels

Semantic event detection is an active and interesting research topic in the field of video mining. The major challenge is the semantic gap between low-level features and high-level semantics. In this chapter, we will advance a new sports video mining framework where a hybrid generative-discriminative approach is used for event detection. Specifically, we propose a three-layer semantic space by which event detection is converted into two inter-related statistical inference procedures that involve semantic analysis at different levels. The first is to infer the mid-level semantic structures from the low-level visual features via generative models, which can serve as building blocks of high-level semantic analysis. The second is to detect high-level semantics from mid-level semantic structures using discriminative models, which are of direct interests to users. In this framework we can explicitly represent and detect semantics at different levels. The use of generative and discriminative approaches in two different stages is proved to be effective and appropriate for event detection in sports video. The experimental results from a set of American football video data demonstrate that the proposed framework offers promising results compared with traditional approaches.

Multimedia Content Protection Technology

2009 ◽  
pp. 957-964
Author(s):  
Shiguo Lian
Keyword(s):  
Video Compression ◽  
Multimedia Data ◽  
Multimedia Content ◽  
Network Technology ◽  
Or Education ◽  
Content Protection ◽  
Tv Program ◽  
Protection Technology ◽  
Multimedia Content Protection ◽  
Media Data

Since the beginning of 1990s, some multimedia standards (Joan, Didier, & Chad, 2003) related to image compression, video compression, or audio compression have been published and widely used. These compression methods reduce media data’s volumes, and save the storage space or transmission bandwidth. After the middle of 1990s, network technology has been rapidly developed and widely spread, which increases the network bandwidth. With the development of network technology and multimedia (image, audio, video, etc.) technology, multimedia data are used more and more widely. In some applications related to politics, economics, militaries, entertainment, or education, multimedia content security becomes important and urgent. Some sensitive data need to be protected against unauthorized users. For example, only the customers paying for a TV program can watch the program online, while other customers cannot watch the content, only the administrator can update (delete, insert, copy, etc.) the TV program in the database, while others cannot modify the content, the TV program released over Internet can be traced, and so forth. Multimedia content protection technology protects multimedia data against the threats coming from unauthorized users, especially in network environment. Generally, protected properties include the confidentiality, integrity, ownership, and so forth. The confidentiality defines that only the authorized users can access the multimedia content, while others cannot know multimedia content. The integrity tells whether media data are modified or not. The ownership shows media data’s owner information that is used to authenticate or trace the distributor. During the past decade, various technologies have been proposed to protect media data, which are introduced in this chapter. Additionally, the threats to multimedia data are presented, the existing protection methods are compared, and some future trends are proposed.

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