Authors:
Hatem Aouadi
;
Mouna Torjmen Khemakhem
and
Maher Ben Jemaa
Affiliation:
National School of Engineering of Sfax, Tunisia
Keyword(s):
Context-based Image Retrieval, Implicit Links, Link Analysis, LDA.
Related
Ontology
Subjects/Areas/Topics:
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Multimedia Systems
;
Performance Evaluation and Benchmarking
Abstract:
In context-based image retrieval, the textual information surrounding the image plays a main role in image retrieval. Although text-based approaches outperform content-based retrieval approaches, they can fail when query keywords are not matching the document content. Therefore, using only keywords in the retrieval process is not sufficient to have good results. To improve the retrieval accuracy, researchers proposed to enhance search accuracy by exploiting other contextual information such as hyperlinks that reflect a topical similarity between documents. However, hyperlinks are usually sparse and do not guarantee document content similarity (advertising and navigational hyperlinks). In addition, there are many missed links between similar documents (only few semantic links are created manually). In this paper, we propose to automatically create implicit links between images through computing the semantic similarity between the textual information surrounding those images. We studie
d the effectiveness of the links generated automatically in the image retrieval process. Results showed that combining different textual representations of the image is more suitable for linking similar images.
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