LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
This addresses the challenge of sketch ambiguity in visual search for users, representing an incremental improvement over existing methods.
The paper tackles the problem of ambiguous sketch-based image search by introducing LiveSketch, an algorithm that provides real-time visual suggestions to iteratively disambiguate queries as they are drawn, resulting in improved accuracy and time-to-task over baselines on a 67M image corpus.
LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.