Searching visual objects in large image or video data sets is a challenging problem, because it requires efficient matching and accurate localization of query objects that often occupy a small part of an image. Although spatial context has been shown to help produce more reliable detection than methods that match local features individually, how to extract appropriate spatial context remains an open problem. Instead of using fixed-scale spatial context, we propose a randomized approach to deriving spatial context, in the form of spatial random partition. The effect of spatial context is achieved by averaging the matching scores over multiple random patches. Our approach offers three benefits: 1) the aggregation of the matching scores over multiple random patches provides robust local matching; 2) the matched objects can be directly identified on the pixelwise confidence map, which results in efficient object localization; and 3) our algorithm lends itself to easy parallelization and also allows a flexible tradeoff between accuracy and speed through adjusting the number of partition times. Both theoretical studies and experimental comparisons with the state-of-the-art methods validate the advantages of our approach.