This paper proposes a novel recognition scheme algorithm for semantic labeling of 2D object present in still images. The
principle consists of matching unknown 2D objects with categorized 3D models in order to infer the semantics of the 3D
object to the image. We tested our new recognition framework by using the MPEG-7 and Princeton 3D model databases
in order to label unknown images randomly selected from the web. Results obtained show promising performances, with
recognition rate up to 84%, which opens interesting perspectives in terms of semantic metadata extraction from still
images/videos.
This paper proposes a comprehensive overview of state of the art 2D/3D, view-based indexing methods. The principle of
2D/3D indexing methods consists of describing 3D models by means of a set of 2D shape descriptors, associated with a
set of corresponding 2D views (under the assumption of a given projection model). Notably, such an approach makes it
possible to identify 3D objects of interest from 2D images/videos. An experimental evaluation is also proposed, in order
to examine the influence of the number of views and of the associated viewing angle selection strategies on the retrieval
results. Experiments concern both 3D model retrieval and image recognition from a single view. Results obtained show
promising performances, with recognition rates from a single view higher then 66%, which opens interesting
perspectives in terms of semantic metadata extraction from still images/videos.
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