: Samples of photo-sketch pairs from the Sketchy database. We show two photos from the Squirrel, Kangaroo, Elephant, Teapot, Cat, and Hedgehog categories. Below each photo are two sketches produced by crowd workers. Notice the variation in sketches across object instances and between artists. We use the Sketchy database as training and test data for sketch-based image retrieval. We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us finegrained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both handcrafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis.
Sangkloy et al. REF later were the first to source a large scale collection of image-sketch pairs dubbed the Sketchy Database, consisting of 125 object classes.
The sketchy database: learning to retrieve badly drawn bunnies
TOGS
Computer Science