Semantic access to a database of images: an approach to object-related image retrieval

Image retrieval has commonly been attempted using non-semantic approaches. It is clear though, that semantic retrieval is more desirable because it facilitates the user´s task. We present a new approach to semantic access 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). This platform is used to automatically index images of a given database by object names, which finally allows the use of semantics (driven by these object names) to extract images from the database (e.g. “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 1,300 images is presented