Improving Shape-Based CBIR for Natural Image Content Using a Modified GFD – Springer

JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. We present a modified version of the Generic Fourier Descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduce the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide for a more accurate similarity ranking of retrieved results, demonstrating greater consideration for dominant internal and external shape details. Folkers, A., Samet, H.: Content-based image retrieval using Fourier descriptors on a logo database. In: Proc. the 16th International Conference on Pattern Recognition, Quebec City, Canada, August 2002, vol. 3, pp. 521–524 (2002) Jarrah, K., Muneesawang, P., Lee, I., Guan, L.: Minimizing human-machine interactions in automatic image retrieval. In: Proc. Canadian Conference on Electrical and Computer Engineering, Niagra Falls, Canada, May 2004, vol. 3, pp. 1589–1592 (2004) Zhang, D.S., Lu, G.: A comparative study of three region shape descriptors. In: DICTA 2002: Digital Image Computing Techniques and Applications, Melbourne, Australia (January 2002) Zhang, D.S., Lu, G.: Generic Fourier Descriptor for Shape-based Image Retrieval. In: Proceedings of IEEE Int. Conf. On Multimedia and Expo., August 2002, vol. 1, pp. 425–428 (2002) Wong, H.S., Guan, L.: A neural learning approach for adaptive image restoration using a fuzzy model-based network architecture. IEEE Transaction on Neural Networks 12, 516–531 (2001) Source.


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