Visual semantics: extracting visual information from text accompanying pictures
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Image retrieval by hypertext links
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Visual information retrieval from large distributed online repositories
Communications of the ACM
Automatic caption localization for photographs on World Wide Web pages
Information Processing and Management: an International Journal
The indexing and retrieval of document images: a survey
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Unifying textual and visual cues for content-based image retrieval on the World Wide Web
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Web mining for web image retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Web image retrieval using self-organinzing feature map
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Modern Information Retrieval
Semantics in Visual Information Retrieval
IEEE MultiMedia
Semantic Modeling and Knowledge Representation in Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
The Terminological Image Retrieval Model
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Semantic Content Based Image Retrieval Using Object-Process Diagrams
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Multimedia Semantic Features and Image Content Description
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
Construction of supervised and unsupervised learning systems for multilingual text categorization
Expert Systems with Applications: An International Journal
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Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the 'semantic gap' between the extracted features of the systems and the user's query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. Most existing techniques apply some predefined semantic categories and assign the images to appropriate categories through some learning processes. Nevertheless, these techniques always need human intervention and rely on content-based features. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. Since an image generally co-exists with accompanying texts in various formats, we may rely on such environmental texts to discover the semantics of the image. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.