Query-Adaptive Small Object Search Using Object Proposals and Shape-Aware Descriptors

While there has been a significant amount of work on object search and image retrieval, the focus has primarily been on establishing effective models for the whole images, scenes, and objects occupying a large portion of an image. In this paper, we propose to leverage object proposals to identify small and smooth-structured objects in a large image database. Unlike popular methods exploring a coarse image-level pairwise similarity, the search is designed to exploit the similarity measures at the proposal level. An effective graph-based query expansion strategy is designed to assess each of these better matched proposals against all its neighbors within the same image for a precise localization. Combined with a shape-aware feature descriptor EdgeBoW, a set of more insightful edge-weights and node-utility measures, the proposed search strategy can handle varying view angles, illumination conditions, deformation, and occlusion efficiently. Experiments performed on a number of other benchmark datasets show the powerful and superior generalization ability of this single integrated framework in dealing with both clutter-intensive real-life images and poor-quality binary document images at equal dexterity.