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Image indexing is one of the most difficult challenges facing the computer vision community. Addressing this issue, we designed an innovative approach to obtain an accurate label for images by taking into account the social aspects of human-based computation. The proposed approach is highly discriminative in comparison to an ordinary content-based image(More)
We introduce an interactive framework for image understanding, a game that is enjoyable and provide valuable image annotations. When people play the game, they provide useful information about contents of an image. In reality the most accurate method to describe the content of an image is manual labelling. Our approach is to motivate people to label imagers(More)
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