Text Versus Audio-Visual Data Representation Techniques

Read Complete Research Material

TEXT VERSUS AUDIO-VISUAL DATA REPRESENTATION TECHNIQUES

Text Versus Audio-visual Data representation Techniques

ABSTRACT

In this paper, Audio-visual representation for information structures in procedure for component-based of information and multi-agent systems are presented, simultaneously with Audio-visual reviewer based on Constraint Graph environment. Moreover, translator is described which translates these Audio-visual representations to textual representations. The strength of blended natural environment is powerful—yet easy-to-use—framework to support development of knowledge-based and multi-agent systems. Finally, mapping is presented to Conceptual Graphs. This provides unifying perspective on information representation format and allows use of conceptual graph tools to specify and sustain information representation part of specification.

Table of Coontent

ABSTRACTII

CHAPTER 1: INTRODUCTION1

1.1Problem Statement1

1.2Rationale1

1.3Aims and Objectives2

1.4Significance2

1.5Research Question3

1.6Limitation of Study3

1.7Reliability4

1.8Validity4

1.9Ethical Concern5

CHAPTER 2: LITERATURE REVIEW7

2.1The audio-visual headset17

2.2Visual front end processing18

2.3ROI extraction on headset captured videos22

2.4ROI extraction on full-face videos24

2.5Visual feature representation26

2.6External representations in learning scenarios27

2.7Instructional support using external representations27

2.8Collaboration-specific support28

2.9Content-specific support29

2.10Dynamic visual speech features31

2.11Visual Speech32

CHAPTER 03: METHODOLOGY34

3.1Participants34

3.2Instrument and data collection methods34

3.3Experimental procedure35

CHAPTER 04: DISCUSSION37

4.1Criteria37

4.2Analysis of judgment estimation39

4.3Findings40

4.4Use of information criteria across two stages41

4.5Use of textual descriptions across two stages46

4.6Difference of importance ratings of each attribute between pre-test and post-test47

4.7Additional findings48

4.8Criteria for not relevant items48

4.9Difference of importance ratings between partially useful items and highly useful items50

4.10Criteria for relevance judgments applied for image content across information-seeking process51

4.11Change of relative importance of criteria across information-seeking process55

4.12Key elements in textual descriptions of visual documents for relevance judgments56

CHAPTER 05: CONCLUSION64

REFERENCES69

CHAPTER 1: INTRODUCTION

Problem Statement

Most dialects for information acquisition, elicitation, and reasoning outcome in specifications in untainted text format. Textual representation is simpler for computer program to process. However, textual representation is not an effortlessly understandable pattern, particularly for those domain professionals who are not well renowned with computer programming. Visual representation of information relies on graphics other than text. Visual representations are more understandable and clear than textual representations

Rationale

Subsequently, Audio-visual representation procedure for information organisations has been evolved, motivated by number of renowned Audio-visual formalisms for example semantic systems, terminological formalisms and conceptual graph formalism . Finally, beginning with , connection of Audio-visual representations to conceptual graph formalism and was enquired in more depth. One of conclusions of present paper is that transformation to conceptual graph formalism can be made that presents apt unifying viewpoint on distinct notations, and makes it likely to use conceptual graph devices to identify and sustain information representation part of DESIRE specification. In some situations, Audio-visual notation of conceptual graphs has been acclimatized bit to get more dedicated Audio-visual format for desires as considered above.

Aims and Objectives

Originally, textual information representation dialect was utilised in DESIRE that is founded on alignment arranged predicate reasoning and some extensions. In specific constructs were presented for following:

Structuring in (hierarchical) constituents of data structure specifications and of information specifications

Explicit calling of data structure specification constituents and information groundwork specification components

Complex object organisations as periods, founded on functions.

The use of meta-descriptions of data organisations inside (other) data structure specifications and information specifications

The use of ordered connections (if-then implications) in information specification

Significance

Constraint Graphs is notion mapping 'meta-language' that permits visual delineation of any number of goal notion mapping ...
Related Ads