Characterization and assessment of fabric smoothness appearance based on sparse coding

Traditional computer vision methods cannot match human performance well on fabric smoothness classification, as this is a subjective assessment based on sparse, comprehensive and low-cost visual perception. This paper reports a new assessment method of fabric smoothness appearance, including feature designing and wrinkle classification. A multidimensional feature was designed by generalizing vector quantization of dense scale-invariant feature transform (SIFT) descriptors for sparse coding and max pooling. Sparse coding provides clear understanding about the receptive fields of visual neurons and can build a codebook from the low features. A one-against-rest linear support vector machine (SVM) was utilized to classify the nine grades of smoothness and quantized to level 0.1 by space distance. Results showed that the proposed approach achieved remarkable classification accuracy in comparison with Bag-of-Feature (BOF) and Spatial Pyramid Matching (SPM) algorithms.