Randomized texture flow estimation using visual similarity

Exploring underlying texture flows defined with orientation and scale is of a great interest on a variety of vision-related tasks. However, existing methods often fail to capture accurate flows due to over-parameterization of texture deformation or employ a costly global optimization which makes the algorithm computationally demanding. In this paper, we address this inverse problem by casting it as a randomized correspondence search along with a locally-adaptive vector field smoothing. When a small example patch is given as a reference, a randomized deformable matching is performed on the very densely quantized label space, enabling an efficient estimation of texture deformation without quality degeneration, e.g., due to quantization artifacts which often appear in the optimization-driven discrete approaches. The visual similarity with respect to the deformation parameters is directly measured with an input texture image on an appearance space. The locally-adaptive smoothing is then applied to the intermediate flow field, resulting in a good continuation of the resultant texture flow. Experimental results on both synthetic and natural images show that the proposed method improves the performance in terms of both runtime efficiency and/or visual quality, compared to the existing methods.