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We introduced a new approach to classify 3D texture images based on output selection from two classification methods. For a set of real-world textured images, we found that, using classification based only on texture information, some images obtained under unknown conditions appeared to have similar surfaces and could not be recognized correctly. Another classification method, based on color information, was therefore performed in parallel to solve such a problem. Some knowledge is required to efficiently select outputs from texture-based classification (TBC) and color-based classification (CBC). Reliability tables (RT), created in the pre-testing stage (PTS) of system training, are proposed to obtain that knowledge. Performance is evaluated by employing over 5600 texture images corresponding to 61 real-world surface samples extracted from the Columbia-Utrecht reflectance and texture (CUReT) database K.J. Dana et al., 1999. Our experiments produce better classification results than those in M. Varma and A. Zisserman (2005), which are the best results available to date.
Image Processing, 2005. ICIP 2005. IEEE International Conference on (Volume:2 )
Date of Conference: 11-14 Sept. 2005