Sketch Based Retrieval with Asymmetric Feature Maps
(Center for Machine Perception, CTU Prague, Czech Republic)
In this work we propose a short vector image representation that supports
efficient scale and translation invariant sketch-based image retrieval. The
efficiency of the search is boosted by two means: approximating a trigonometric
polynomial of scores and by decomposing a 2D translation search by 1D
projections. We introduce a novel concept of asymmetric feature maps, which
allows to evaluate multiple kernels without increasing memory requirements. This
enables multi-scale search as well as translation approximation by projections.
The representation is learned by joint feature maps optimization.