  The contextual information of Web images is investigated to address the issue
of characterizing their content with semantic descriptors and therefore bridge
the semantic gap, i.e. the gap between their automated low-level representation
in terms of colors, textures, shapes. . . and their semantic interpretation.
Such characterization allows for understanding the image content and is crucial
in important Web-based tasks such as image indexing and retrieval. Although we
are highly motivated by the availability of rich knowledge on the Web and the
relative success achieved by commercial search engines in automatically
characterizing the image content using contextual information in Web pages, we
are aware that the unpredictable quality of the contextual information is a
major limiting factor. Among the reasons explaining the difficulty to leverage
on the image contextual information, some problems are related to the
characterization and extraction of this information. Indeed, the first issue is
the lack of large-scale studies to highlight what is considered the relevant
contextual information of an image, where it is located in a Web page and
whether it is consistent across Web pages of different types, content layouts
and domains. Also, the matter related to the extraction of this contextual
information is topical as state-of-the-art automated extraction tools are
unable to handle the heterogeneous Web. As far as the processing of the
contextual information is concerned, problems linked to the syntactic and
semantic characterizations of the textual components are important to address
in order to tackle the semantic gap. Furthermore, questions pertaining to the
organization of these textual components into coherent structures that are
usable in image indexing and retrieval frameworks shall arise.
