Compositional Sketch Search

• URL: http://arxiv.org/abs/2106.08009v1
• Date: Tue, 15 Jun 2021 09:38:09 GMT
• Title: Compositional Sketch Search
• Authors: Alexander Black, Tu Bui, Long Mai, Hailin Jin, John Collomosse
• Abstract summary: We present an algorithm for searching image collections using free-hand sketches. We exploit drawings as a concise and intuitive representation for specifying entire scene compositions.
• Score: 91.84489055347585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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