Search-based image annotation: Extracting semantics from similar images
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Experimental IR Meets Multilinguality, Multimodality, and Interaction - 6th International Conference of the CLEF Association, CLEF 2015 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-24027-5_36 |
| Field | Informatics |
| Keywords | image annotation; similarity search; evaluation |
| Description | The importance of automatic image annotation as a tool for handling large amounts of image data has been recognized for several decades. However, working tools have long been limited to narrow-domain problems with a few target classes for which precise models could be trained. With the advance of similarity searching, it now becomes possible to employ a different approach: extracting information from large amounts of noisy web data. However, several issues need to be resolved, including the acquisition of a suitable knowledge base, choosing a suitable visual content descriptor, implementation of effective and efficient similarity search engine, and extraction of semantics from similar images. In this paper, we address these challenges and present a working annotation system based on the search-based paradigm, which achieved good results in the 2014 ImageCLEF Scalable Concept Image Annotation challenge. |
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