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Our research addresses the need for an efficient, effective, and interactive access to large-scale image collections. Image retrieval needs are evolving beyond the capabilities of the traditional indexing based on manual annotation, and the most desirable ...
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hi ...
This paper provides a general introduction to the concept of Implicit Human-Centered Tagging (IHCT) - the automatic extraction of tags from nonverbal behavioral feedback of media users. The main idea behind IHCT is that nonverbal behaviors displayed when i ...
Content-based image retrieval systems have to cope with two different regimes: understanding broadly the categories of interest to the user, and refining the search in this or these categories to converge to specific images among them. Here, in contrast wi ...
Content-based image retrieval aims at substituting traditional indexing based on manual annotation by using automatically-extracted visual indexing features. Novel techniques are needed however to efficiently deal with the semantic gap (i.e. the partial ma ...
2016
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It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hi ...
