Abstract
Recently many researchers are interested in objects of interest in an image, which are useful for efficient image matching based on them and bridging the semantic gap between higher concept of users and low-level image features. In this paper, we introduce a computational approach that classifies an object of interest into a natural or a man-made class, which can be of great interest for semantic indexing applications processing very large image databases. We first show that Gabor energy maps for man-made objects tend to have dominant orientation features through analysis of Gabor filtering results for many object images. Then a sum of Gabor orientation energy differences is proposed as a classification measure, which shows a classification accuracy of 82.9% in a test with 2,600 object images.
This is a preview of subscription content, log in via an institution to check access.
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
Vailaya, A., Jain, A.K., Zhang, H.J.: On Image Classification: City Images vs. Landscape. Pattern Recognition 31(12), 1921–1936 (1998)
Szummer, M., Picard, R.W.: Indoor-Outdoor Image Classification. In: IEEE Int’l Workshop Content-Based Access Image Video Databases, pp. 42–51 (1998)
Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J.: Image Classification for Content-Based Indexing. IEEE Trans. on Image Processing 10(1), 117–130 (2001)
Oliva, A., Torralba, A.B., Gurin-Dugue, A., Herault, J.: Global Semantic Classification of Scenes Using Power Spectrum Templates. In: Challenge of Image Retrieval (CIR 1999), Newcastle UK (1999)
Osberger, W., Maeder, A.J.: Automatic Identification of Perceptually Important Regions in an Image. In: IEEE Int’l Conf. on Pattern Recognition, pp. 701–704 (1998)
Huang, Q., Dom, B., Steels, D., Ashely, J., Niblack, W.: Foreground / Background Segmentation of Color Images by Integration of Multiple Cues. In: Int’l Conf. on Image Processing., vol. 1, pp. 246–249 (1995)
Serra, J.R., Subirana, J.B.: Texture Frame Curves and Regions of Attention Using Adaptive Non-cartesian Networks. Pattern Recognition 32, 503–515 (1999)
Kim, S., Park, S., Kim, M.: Central Object Extraction for Object-Based Image Retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 39–49. Springer, Heidelberg (2003)
Kim, S., Park, S., Kim, M.: Image Classification into Object / Non-object Classes. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 393–400. Springer, Heidelberg (2004)
Park, S.B., Lee, J.W., Kim, S.K.: Content-Based Image Classification Using a Neural Network. Pattern Recognition Letter 25, 287–300 (2004)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Trans. on Pattern Analysis and Machine Intelligence. 18(8), 837–842 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, M., Park, C., Koo, K. (2005). Natural / Man-Made Object Classification Based on Gabor Characteristics. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_58
Download citation
DOI: https://doi.org/10.1007/11526346_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27858-0
Online ISBN: 978-3-540-31678-7
eBook Packages: Computer ScienceComputer Science (R0)