Unsupervised texture segmentation and labeling using biologically inspired features

Due to the semantic gap, describing high-level semantic concepts with low-level visual features is a very challenging task. The classification of textures in scene images is intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired features for texture segmentation and an unsupervised method to link those texture features with semantic concepts. The calculation of the features is inspired by the human visual system and corresponds to cell outputs in the first stage of the visual cortex. Analogously to the processing principles of the cortex, self-organizing maps are employed for classification. The performance of the texture segmentation and labeling is evaluated on textures from the Brodatz album and on a real-life scenery image dataset. For both methods, a high percentage of pixels is correctly classified.