Image retrieval has been commonly attempted using non-semantic approaches. It is clear though, that semantic retrieval is more desirable because it facilitates the user's task. In this paper, we present a new appr...
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Image retrieval has been commonly attempted using non-semantic approaches. It is clear though, that semantic retrieval is more desirable because it facilitates the user's task. In this paper, we present a new approach to semanticaccess of a database of images by asking for the presence of certain objects;this is known as object-related image retrieval. This approach is built within a classical computer vision framework (i.e. localization, segmentation and identification). Our approach first searches for the main areas of attention (most salient areas of an image) and then applies appearance-based methods to classify (index) all images by 'symbolic' names. These names are referred to objects, which finally allows the use of semantics driven by these object names, e.g. retrieve 'all those images that have a bull and Melissa's face'. The use of a totally automatic system would cause some errors of indexing (and so retrieval). To solve this we use a human-in-the-loop strategy where a human expert is placed after the two outputs of the system to confirm. their 'correctness'. An experimental result using a database of 3000 images is presented. (C) 2000 Academic Press
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