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

Abstract— 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 smoothstructured 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 bettermatched 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.< final year projects >