A model-based approach to semantic-based retrieval of visual information

Forouzan Golshani, Youngchoon Park, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Visual context descriptor (VCD) is a new image representation scheme for visual content classification. It consists of a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region thereof. VCD utilizes the predetermined quality dimensions, such as types of features and quantization level, along with predetermined semantic model templates. The observed visual cues and the contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector, say a color histogram or a Gabor texture, into a discrete event, e. g., terms in the text domain.

Original languageEnglish (US)
Title of host publicationSOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings
PublisherSpringer Verlag
Pages149-167
Number of pages19
Volume2540 LNCS
ISBN (Print)354000145X, 9783540001454
DOIs
StatePublished - 2002
Event29th Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2002 - Milovy, Czech Republic
Duration: Nov 22 2002Nov 29 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2540 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other29th Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2002
CountryCzech Republic
CityMilovy
Period11/22/0211/29/02

Fingerprint

Retrieval
Semantics
Model-based
Textures
Color
Descriptors
Vision
Color Histogram
Image Representation
Discrete Event
Correlation Analysis
Feature Vector
Texture
Template
Quantization
Term
Context

Keywords

  • Contextual image matching
  • Ontology-based image retrieval
  • Semantic image classification
  • Visual cue co-occurrence

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Golshani, F., Park, Y., & Panchanathan, S. (2002). A model-based approach to semantic-based retrieval of visual information. In SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings (Vol. 2540 LNCS, pp. 149-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2540 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-36137-5_9

A model-based approach to semantic-based retrieval of visual information. / Golshani, Forouzan; Park, Youngchoon; Panchanathan, Sethuraman.

SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings. Vol. 2540 LNCS Springer Verlag, 2002. p. 149-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2540 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Golshani, F, Park, Y & Panchanathan, S 2002, A model-based approach to semantic-based retrieval of visual information. in SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings. vol. 2540 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2540 LNCS, Springer Verlag, pp. 149-167, 29th Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2002, Milovy, Czech Republic, 11/22/02. https://doi.org/10.1007/3-540-36137-5_9
Golshani F, Park Y, Panchanathan S. A model-based approach to semantic-based retrieval of visual information. In SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings. Vol. 2540 LNCS. Springer Verlag. 2002. p. 149-167. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-36137-5_9
Golshani, Forouzan ; Park, Youngchoon ; Panchanathan, Sethuraman. / A model-based approach to semantic-based retrieval of visual information. SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings. Vol. 2540 LNCS Springer Verlag, 2002. pp. 149-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{bf03c44a5fbe4a9d909611d8a3d54415,
title = "A model-based approach to semantic-based retrieval of visual information",
abstract = "Visual context descriptor (VCD) is a new image representation scheme for visual content classification. It consists of a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region thereof. VCD utilizes the predetermined quality dimensions, such as types of features and quantization level, along with predetermined semantic model templates. The observed visual cues and the contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector, say a color histogram or a Gabor texture, into a discrete event, e. g., terms in the text domain.",
keywords = "Contextual image matching, Ontology-based image retrieval, Semantic image classification, Visual cue co-occurrence",
author = "Forouzan Golshani and Youngchoon Park and Sethuraman Panchanathan",
year = "2002",
doi = "10.1007/3-540-36137-5_9",
language = "English (US)",
isbn = "354000145X",
volume = "2540 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "149--167",
booktitle = "SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings",
address = "Germany",

}

TY - GEN

T1 - A model-based approach to semantic-based retrieval of visual information

AU - Golshani, Forouzan

AU - Park, Youngchoon

AU - Panchanathan, Sethuraman

PY - 2002

Y1 - 2002

N2 - Visual context descriptor (VCD) is a new image representation scheme for visual content classification. It consists of a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region thereof. VCD utilizes the predetermined quality dimensions, such as types of features and quantization level, along with predetermined semantic model templates. The observed visual cues and the contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector, say a color histogram or a Gabor texture, into a discrete event, e. g., terms in the text domain.

AB - Visual context descriptor (VCD) is a new image representation scheme for visual content classification. It consists of a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region thereof. VCD utilizes the predetermined quality dimensions, such as types of features and quantization level, along with predetermined semantic model templates. The observed visual cues and the contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector, say a color histogram or a Gabor texture, into a discrete event, e. g., terms in the text domain.

KW - Contextual image matching

KW - Ontology-based image retrieval

KW - Semantic image classification

KW - Visual cue co-occurrence

UR - http://www.scopus.com/inward/record.url?scp=84886431406&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886431406&partnerID=8YFLogxK

U2 - 10.1007/3-540-36137-5_9

DO - 10.1007/3-540-36137-5_9

M3 - Conference contribution

AN - SCOPUS:84886431406

SN - 354000145X

SN - 9783540001454

VL - 2540 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 149

EP - 167

BT - SOFSEM 2002: Theory and Practice of Informatics - 29th Conference on Current Trends in Theory and Practice of Informatics, Proceedings

PB - Springer Verlag

ER -