Query Adaptive Multiview Object Instance Search and Localization Using Sketches
Sketch-based object search is a challenging problem mainly due to three difficulties: 1) how to match the primary sketch query with the colorful image; 2) how to locate the small object in a big image that is similar to the sketch query; and 3) given the large image database, how to ensure an efficient search scheme that is reasonably scalable. To address the above challenges, we propose leveraging object proposals for object search and localization. However, instead of purely relying on sketch features, we propose fully utilizing the appearance features of object proposals to resolve the ambiguities between the matching sketch query and object proposals. Our proposed query adaptive search is formulated as a subgraph selection problem, which can be solved by the maximum flow algorithm. By performing query expansion, it can accurately locate the small target objects in a cluttered background or densely drawn deformation-intensive cartoon (Manga like) images. To improve the computing efficiency of matching proposal candidates, the proposed Multi View Spatially Constrained Proposal Selection encodes each identified object proposal in terms of a small local basis of anchor objects. The results on benchmark datasets validate the advantages of utilizing both the sketch and appearance features for sketch-based search, while ensuring sufficient scalability at the same time.