Retrieval by classification of images containing large manmade objects using perceptual grouping

Abstract This paper applies perceptual grouping rules to the retrieval by classification of images containing large manmade objects such as buildings, towers, bridges, and other architectural objects. The semantic interrelationships between primitive image features are exploited by perceptual grouping to extract structure to detect the presence of manmade objects. Segmentation and detailed object representation are not required. The system analyzes each image to extract features that are strong evidence of the presence of these objects. These features are generated by the strong boundaries typical of manmade structures: straight line segments, longer linear lines, coterminations, “L” junctions, “U” junctions, parallel lines, parallel groups, “significant” parallel groups, cotermination graph, and polygons. A K -nearest neighbor framework is employed to classify these features and retrieve the images that contain manmade objects. Results are demonstrated for two databases of monocular outdoor images.