Relevance Feedback for Image Retrieval : a Short Survey

The difficulty and cost of providing rich and reliable textual annotations for images in large databases, as well as the “linguistic gap” associated to these annotations, explains why the retrieval of images based directly on their visual content (content-based image retrieval, CBIR) is of high interest today [16]. In the early years of research in CBIR, the focus was on query by visual example (QBVE): a search session begins by presenting an example image (or sketch) to the search engine as a visual query, then the engine returns images that are visually similar to the query image. More recently, the concept of semantic gap has been extensively used in the CBIR research community to express the discrepancy between the low-level features that can be readily extracted from the images and the descriptions that are meaningful for the users. The automatic association of such descriptions to the low-level features is currently only feasible for very restricted domains and applications. When searching more generic image databases, one way of identifying what the user is looking for in the current retrieval session (the target of the user) is by including the user in the retrieval loop. For this, the session is divided into several consecutive rounds; at every round the user provides feedback regarding the retrieval results, e.g. by qualifying images returned as either “relevant” or “irrelevant” (relevance feedback or RF in the following); from this feedback, the engine learns the visual features of the images and returns

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