Summary
At present, there are vast amounts of digital media available on the web. In the Web image retrieval, the semantics of an image is a big problem, generally, the search engines index the text associated to the image of Web pages. This text doesn’t correspond really to them.
The image annotation is an effective technology for improving the Web image retrieval. Indeed, it permits assigning semantics to an image, by attributing to the images keywords corresponding to the senses conveyed by these images. To improve the automatic image annotation (AIA), a strategy consists in correlating the textual and visual information of the images. In this work, we propose an image auto-annotation system based on AnnotB-LSA algorithm that integrates the LSA model.
The main focus of this paper is two-fold. First, in the training stage, we perform clustering of regions into classes of similar visual regions called blobs according to their visual feature. This clustering prepares a visual space by learning from the annotated images corpus and permits to annotate the blobs by performing the algorithm annotB-LSA. Second, in the new image annotation stage, we can annotate a new image by selecting the key words of the blobs to which its regions belong. Experiment results show that our proposed system is performing.
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Bakalem, M., Benblidia, N., Ait-Aoudia, S. (2011). A Novel Image Auto-annotation Based on Blobs Annotation. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_13
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DOI: https://doi.org/10.1007/978-3-642-23154-4_13
Publisher Name: Springer, Berlin, Heidelberg
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