Common Landmark Discovery for Object-Level View Image Retrieval: Modeling and Matching of Scenes via Bag-of-Bounding-Boxes

Abstract

Object-level view image retrieval for robot vision applications has been actively studied recently, as they can provide semantic and compact method for efficient scene matching. In existing frameworks, landmark objects are extracted from an input view image by a pool of pretrained object detectors, and used as an image representation. To improve the compactness and autonomy of object-level view image retrieval, we here present a novel method called ``common landmark discovery". Under this method, landmark objects are mined through common pattern discovery (CPD) between an input image and known reference images. This approach has three distinct advantages. First, the CPD-based object detection is unsupervised, and does not require pretrained object detector. Second, the method attempts to find fewer and larger object patterns, which leads to a compact and semantically robust view image descriptor. Third, the scene matching problem is efficiently solved as a lower-dimensional problem of computing region overlaps between landmark objects, using a compact image representation in a form of bag-of-bounding-boxes (BoBB).