Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases.

The extraction of texture features from images faces two new challenges: large-scale databases with diversified textures, and varying imaging conditions. We propose a novel method termed multiscale blob features (MBF) to overcome these two difficulties. MBF analyzes textures in both resolution scale and gray level. Proposed statistical descriptors effectively extract structural information from the decomposed binary images. Experimental results show that MBF outperforms other methods on combined large-scale databases (VisTex+Brodatz+CUReT+OuTex). Moreover, experimental results on the University of Illinois at Urbana-Champaign database and the entire Brodatzs atlas show that MBF is invariant to gray-level scaling and image rotation, and is robust across a substantial range of spatial scaling. [ABSTRACT FROM AUTHOR]