Paper ID | ARS-9.8 | ||
Paper Title | COMPOSITIONAL SKETCH SEARCH | ||
Authors | Alexander Black, Tu Bui, University of Surrey, United Kingdom; Long Mai, Hailin Jin, Adobe Research, United States; John Collomosse, University of Surrey, United Kingdom | ||
Session | ARS-9: Interpretation, Understanding, Retrieval | ||
Location | Area I | ||
Session Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation | Poster | ||
Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Storage and Retrieval | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | We present an algorithm for searching image collections using free-hand sketches that describe the appearance and relative positions of multiple objects. Sketch based image retrieval (SBIR) methods predominantly match queries containing a single, dominant object invariant to its position within an image. Our work exploits drawings as a concise and intuitive representation for specifying entire scene compositions. We train a convolutional neural network (CNN) to encode masked visual features from sketched objects, pooling these into a spatial descriptor encoding the spatial relationships and appearances of objects in the composition. Training the CNN backbone as a Siamese network under triplet loss yields a metric search embedding for measuring compositional similarity which may be efficiently leveraged for visual search by applying product quantization. |