Abstract
Given its importance, the problem of object discovery in High-Resolution Remote-Sensing (HRRS) imagery has been given a lot of attention by image retrieval researchers. Despite the vast amount of expert endeavor spent on this problem, more effort has been expected to discover and utilize hidden semantics of images for image retrieval. To this end, in this paper, 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 new semantic features as starting points, we can improve the performance of object discovery in terms of various external criteria.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnard, K., et al.: Matching words and pictures. Machine learning research 3(1), 1107–1135 (2003)
Duygulu, P., et al.: Object recognition as machine translation: learning a lexcicon for a fixed image vocabulary. In: Heyden, A., et al. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)
Feng, S.L., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: CVPR, pp. 1002–1009 (2004)
Guo, D., Atluri, V., Adam, N.: Texture-based remote-sensing image segmentation. In: ICME, pp. 1472–1475 (2005)
ecognition userguide (2004), http://www.definiens imaging.com/
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: SIGIR, pp. 254–261 (2003)
Lavrenko, V., Choquette, M., Croft, W.: Cross-lingual relevance models. In: SIGIR, pp. 175–182 (2002)
Lavrenko, V., Croft, W.: Relevance-based language models. In: SIGIR, pp. 120–127 (2001)
Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: MISRM (1999)
Sheikholeslami, G., Chang, W., Zhang, A.: Semquery: Semantic clustering and querying on heterogeneous features for visual data. TKDE 14(5), 988–1002 (2002)
Shekhar, S., Chawla, S.: Spatial Databases: A Tour, p. 300. Prentice-Hall, Englewood Cliffs (2003)
Wang, L., et al.: Automatic image annotation and retrieval using weighted feature selection. In: IEEE-MSE, Kluwer Publisher, Dordrecht (2004)
Xiong, H., Tan, P., Kumar, V.: Mining strong affinity association patterns in data sets with skewed support distribution. In: ICDM, pp. 387–394 (2003)
Xiong, H., Tan, P., Kumar, V.: Hyperclique pattern discovery. Data Mining and Knowledge Discovery Journal 13(2), 219–242 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Guo, D., Xiong, H., Atluri, V., Adam, N. (2007). Semantic Feature Selection for Object Discovery in High-Resolution Remote Sensing Imagery. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-71701-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)