Texture image retrieval and similarity matching

• Published in: Proceedings of 2004 International Conference on Machine Learning and Cybernetics : August 6-29, 2004, Worldfield Convention Hotel, Shanghai, China Vol. 7; pp. 4081 - 4084 vol.7
• Main Authors: Zhao-Wei Shang, Gui-Zhong Liu, Ya-Tong Zhou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Anisotropic magnetoresistance; Discrete wavelet transforms; Histograms; Image retrieval; Image texture analysis; Information retrieval; Signal resolution; Spatial resolution; Stochastic processes; Wavelet transforms
• ISBN: 0780384032; 9780780384033
• DOI: 10.1109/ICMLC.2004.1384554

Texture is one of the most important visual characteristics, which play a very critical role in many tasks, ranging from remote sensing to medical imaging and CBIR. The texture analysis has a long history and how to extract texture feature efficiently and accurately is still an active subject of study in the field of image retrieval. June proposed a method that used two features, and one of them is the gray-level histogram based on each low frequency. The gray-level histogram does not take the spatial relationship of gray in an image into account. In this paper, we establish a new way that describes texture in terms of their orientations and original image gray distributions using geostat, which represents the global spatial relationship of color. The performance has raised about 4% than that of June's method.