VisualNet: commonsense knowledgebase for video and image indexing and retrieval application

Alabdullah Altadmri, Amjad and Ahmed, Amr (2009) VisualNet: commonsense knowledgebase for video and image indexing and retrieval application. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, 21-22 November 2009, Shanghai, China..

Documents
VisualNet: commonsense knowledgebase for video and image indexing and retrieval application
[img]
[Download]
[img]
Preview
PDF
VisualNet.pdf

238Kb

Abstract

The rapidly increasing amount of video collections, available on the web or via broadcasting, motivated research towards building intelligent tools for searching, rating, indexing and retrieval purposes. Establishing a semantic representation of visual data, mainly in textual form, is one of the important tasks.
The time needed for building and maintaining Ontologies and knowledge, especially for wide domain, and the efforts for integrating several approaches emphasize the need for unified generic commonsense knowledgebase for visual applications.

In this paper, we propose a novel commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. We refer to it as "VisualNet".
VisualNet is obtained by our fully automated engine that constructs a new unified structure concluding the knowledge from two commonsense knowledgebases, namely WordNet and ConceptNet. This knowledge is extracted by performing analysis operations on WordNet and ConceptNet contents, and then only useful knowledge in visual domain applications is considered.
Moreover, this automatic engine enables this knowledgebase to be developed, updated and maintained automatically, synchronized with any future enhancement on WordNet or ConceptNet.

Statistical properties of the proposed knowledgebase, in addition to an evaluation of a sample application results, show coherency and effectiveness of the proposed knowledgebase and its automatic engine.

Item Type:Conference or Workshop Item (Paper)
Additional Information:The rapidly increasing amount of video collections, available on the web or via broadcasting, motivated research towards building intelligent tools for searching, rating, indexing and retrieval purposes. Establishing a semantic representation of visual data, mainly in textual form, is one of the important tasks. The time needed for building and maintaining Ontologies and knowledge, especially for wide domain, and the efforts for integrating several approaches emphasize the need for unified generic commonsense knowledgebase for visual applications. In this paper, we propose a novel commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. We refer to it as "VisualNet". VisualNet is obtained by our fully automated engine that constructs a new unified structure concluding the knowledge from two commonsense knowledgebases, namely WordNet and ConceptNet. This knowledge is extracted by performing analysis operations on WordNet and ConceptNet contents, and then only useful knowledge in visual domain applications is considered. Moreover, this automatic engine enables this knowledgebase to be developed, updated and maintained automatically, synchronized with any future enhancement on WordNet or ConceptNet. Statistical properties of the proposed knowledgebase, in addition to an evaluation of a sample application results, show coherency and effectiveness of the proposed knowledgebase and its automatic engine.
Keywords:Commonsense Knowledgebase, Knowledgebased Systems, Computer Vision, Video Indexing, Visual Indexing, Image Indexing, Commonsense Knowledge bases, Video Retrieval, Video Annotation, Video Databases, Video Databases Annotation, Semantic Video Annotation, visual events, free text annotation, Event Detection, Wide Domain Videos, Automatic Semantic Video Annotation, Video Information Retrieval, Image Annotation
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G710 Speech and Natural Language Processing
G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G450 Multi-media Computing Science
G Mathematical and Computer Sciences > G720 Knowledge Representation
G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G540 Databases
Divisions:College of Science > School of Computer Science
ID Code:2092
Deposited By:INVALID USER
Deposited On:16 Dec 2009 11:34
Last Modified:13 Mar 2013 08:33

Repository Staff Only: item control page