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We propose a novel concept of asymmetric feature maps (AFM), which allows toevaluate multiple kernels between a query and database entries withoutincreasing the memory requirements. To demonstrate the advantages of the AFMmethod, we derive a short vector image representation that, due to asymmetricfeature maps, supports efficient scale and translation invariant sketch-basedimage retrieval. Unlike most of the short-code based retrieval systems, theproposed method provides the query localization in the retrieved image. Theefficiency of the search is boosted by approximating a 2D translation searchvia trigonometric polynomial of scores by 1D projections. The projections are aspecial case of AFM. An order of magnitude speed-up is achieved compared totraditional trigonometric polynomials. The results are boosted by animage-based average query expansion, exceeding significantly the state of theart on standard benchmarks.

Asymmetric Feature Maps with Application to Sketch Based Retrieval