|Résumé||Methods based on local features have been very successful in visual search, especially when the objective is to identify near-identical objects or scenes under occlusion and varying viewpoint or lighting conditions. After a brief introduction to such methods, including bag-of-words models, sub-linear indexing and spatial matching, this talk focuses on recent research results related to local feature detection and the role of geometry, as well as a number of applications.
In particular, we present methods based on image gradient and distance maps that are able to detect blob-like regions of arbitrary scale and shape, and their application to image matching. We then investigate the potential of embeding the spatial matching process within the index, so that it becomes sub-linear as well. We also report on an accelerated spatial matching method for re-ranking, that allows flexible matching of multiple surfaces.
We then move to the more difficult problem of organizing large photo collections, and examine the use of sub-linear indexing in a mining process. Photos are automatically grouped wherever they depict the same scene; this structure is then exploited to increase the recall of the retrieval process. Working on community collections of geo-tagged photos depicting urban scenery, this approach is applied to automatic location and landmark recognition from a single photo. We also present our online application, VIRaL. Finally, we present our C++ template library ivl, that is used as infrastructure in our implementations.