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Many object categories are better characterized by the shape of their contour than by local appearance properties like texture or color. Multi-local features are designed in order to capture the global discriminative structure of an object while at the same time avoiding the drawbacks with traditional global descriptors such as sensitivity to irrelevant image properties. The specific structure of multi-local features allows us to generate new feature exemplars by linear combinations which effectively increases the set of stored training exemplars. We demonstrate that a multi-local feature is a good "weak detector" of shape-based object categories and that it can accurately estimate the bounding box of objects in an image. Using just a single multi-local feature descriptor we obtain detection results comparable to those of more complex and elaborate systems. It is our opinion that multi-local features have a great potential as generic object descriptors with very interesting possibilities of feature sharing within and between classes.