Abstract
Given its importance, the problem of object discovery in high-resolution remote-sensing (HRRS) imagery has received a lot of attention in the literature. Despite the vast amount of expert endeavor spent on this problem, more efforts have been expected to discover and utilize hidden semantics of images for object detection. To that end, in this paper, we address this problem from two semantic perspectives. First, we propose a semantic-aware two-stage image segmentation approach, which preserves the semantics of real-world objects during the segmentation process. Second, to better capture semantic features for object discovery, we exploit a hyperclique pattern discovery method to find complex objects that consist of several co-existing individual objects that usually form a unique semantic concept. We consider the identified groups of co-existing objects as new feature sets and feed them into the learning model for better performance of image retrieval. Experiments with real-world datasets show that, with reliable segmentation and new semantic features as starting points, we can improve the performance of object discovery in terms of various external criteria.