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Supervised texture segmentation using 2D LSTM networks

Segmenting images into different regions based on textures is a difficult task, which is usually approached using a combination of texture classification and image segmentation algorithms. The inherent variability of textured regions makes this a difficult modeling task. This paper show that 2D LSTM networks can solve the texture segmentation problem, combining both texture classification and spatial modeling within a single and trainable model. It directly outputs per-pixel texture classes and does not require a separate feature extraction step. We first introduce a new blob-mosaics texture segmentation dataset and its evaluation criteria, then evaluate our approach on the dataset and compare its performance with existing methods.