Query Adaptive Instance Search using Object Sketches

Abstract

Sketch-based object search is a challenging problem mainly due to two difficulties: (1) how to match the binary sketch query with the colorful image, and (2) how to locate the small object in a big image with the sketch query. To address the above challenges, we propose to leverage object proposals for object search and localization. However, instead of purely relying on sketch features, e.g., Sketch-a-Net, to locate the candidate object proposals, we propose to fully utilize the appearance information to resolve the ambiguities among object proposals and refine the search results. Our proposed query adaptive search is formulated as a sub-graph selection problem, which can be solved by maximum flow algorithm. By performing query expansion using a smaller set of more salient matches as the query representatives, it can accurately locate the small target objects in cluttered background or densely drawn deformation intensive cartoon (Manga like) images. Our query adaptive sketch based object search on benchmark datasets exhibits superior performance when compared with existing methods, which validates the advantages of utilizing both the shape and appearance features for sketch-based search.