Look Closely: Learning Exemplar Patches for Recognizing Textiles from Product Images

The resolution of product images is becoming higher dues to the rapid development of digital cameras and the Internet. Higher resolution images expose novel feature relationships that did not exist before. For instance, from a large image of a garment, one can observe the overall shape, the wrinkles, and the micro-level details such as sewing lines and weaving patterns. The key idea of our work is to combine features obtained at such largely different scales to improve textile recognition performance. Specifically, we develop a robust semi-supervised model that exploits both micro textures and macro deformable shapes to select representative patches from product images. The selected patches are then used as inputs to conventional texture recognition methods to perform texture recognition. We show that, by learning from human-provided image regions, the method can suggest more discriminative regions that lead to higher categorization rates (+5-7%). We also show that our patch selection method significantly improves the performance of conventional texture recognition methods that usually rely on dense sampling. Our dataset of labeled textile images will be released for further investigation in this emerging field.