RAID: A Relation-Augmented Image Descriptor

摘要

As humans, we regularly interpret scenes based on how objects arerelated, rather than based on the objects themselves. For example, we see a personridingan object X or a plankbridgingtwo objects. Current methods provide limited support to search for content based on such relations. We presentraid, a relation-augmented image descriptor that supports queries based on inter-region relations. The key idea of our descriptor is to encode region-to-region relations as the spatial distribution of point-to-region relationships between two image regions.raidallows sketch-based retrieval and requires minimal training data, thus making it suited even for querying uncommon relations. We evaluate the proposed descriptor by querying into large image databases and successfully extract non-trivial images demonstrating complex inter-region relations, which are easily missed or erroneously classified by existing methods. We assess the robustness ofraidon multiple datasets even when the region segmentation is computed automatically or very noisy.