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Pattern Recognition 35 (2002) 14631479 www.elsevier.com/locate/patcog
 

Summary: Pattern Recognition 35 (2002) 1463­1479
www.elsevier.com/locate/patcog
Retrieval by classi˙cation of images containing large
manmade objects using perceptual grouping
Qasim Iqbal, J.K. Aggarwal
Department of Electrical and Computer Engineering, Computer and Vision Research Center,
The University of Texas at Austin, Austin, TX 78712, USA
Received 17 October 2000; accepted 13 July 2001
Abstract
This paper applies perceptual grouping rules to the retrieval by classi˙cation 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, "signi˙cant" 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. ? 2002 Pattern Recognition Society. Published by
Elsevier Science Ltd. All rights reserved.
Keywords: Perceptual grouping; Structure; Content-based image retrieval; Image databases; Multi-media systems; Nearest neighbor

  

Source: Aggarwal, J. K. - Department of Electrical and Computer Engineering, University of Texas at Austin

 

Collections: Computer Technologies and Information Sciences; Engineering