A Sketch-texture Retrieval Framework using Perceptual Similarity.

Sketch-based image retrieval is an important research topic in the field of image processing. Hand-drawn sketches consist only of contour lines, and lack detailed information such as color and textons. As a result, they differ significantly from color images in terms of image feature distribution, making sketch-based image retrieval a typical cross-domain retrieval problem. To solve this problem, we constructed a perceptual space consistent with both textures and sketches, and using perceptual similarity for sketch-based texture retrieval. To implement this approach, we first conduct a set of psychological experiments to analyze the similarity of visual perception of the textures, then we create a dataset of over a thousand hand-drawn sketches according to the textures. We proposed a layer-wise perceptual similarity learning method that integrates perceptual similarity, with which we trained a similarity prediction network to learn the perceptual similarity between hand-drawn sketches and natural texture images. The trained network can be used for perceptual similarity prediction and efficient retrieval. Our experimental results demonstrate the effectiveness of sketch-based texture retrieval using perceptual similarity. • A hand-drawn natural texture image dataset was created. More than a thousand hand-drawn texture sketches are collected. • A free-grouping psychophysical experiment was completed, the texture perceptual similarity matrix is obtained. • A natural texture retrieval framework based on hand-drawn sketches was constructed. We introduced a layer-wise perceptual similarity measurement method and trained a small regression convolutional neural network using paired textures, enabling efficient retrieval based on perceptual similarity from psychophysical experiments. [ABSTRACT FROM AUTHOR]