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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Most of the recent work on image-based object recognition and 3D reconstruction has focused on improving the underlying algorithms. In this paper we present a method to automatically improve the quality of the reference database, which, as we will show, also affects recognition and reconstruction performances significantly. Starting out from a reference database of clustered images we expand small clusters. This is done by exploiting cross-media information, which allows for crawling of additional images. For large clusters redundant information is removed by scene analysis. We show how these techniques make object recognition and 3D reconstruction both more efficient and more precise - we observed up to 14.8% improvement for the recognition task. Furthermore, the methods are completely data-driven and fully automatic.