# Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
## 1 Introduction  
The woven fabric weave pattern is a distinguishable feature for fabric texture recognition, as the fabric weave pattern represents different textures. The fabric weave pattern can be defined statistically, such as using the average value, variance, co-occurrence matrix, and histogram [1]. [PERSON] used the gray-level features to propose a method for texture identification [1]. In some earlier studies, Fourier transformation technology was used to identify the fabric, and the identification accuracy was found to be 90%, but fabrics with similar weave patterns were not identified successfully [2, 3]. In some recent studies, the identification of the warp floats and weft floats from the fabric image were used to determine the fabric texture [4, 5, 6, 7]. [PERSON] et al. used the active-grid-model (AGM) to recognize the weave pattern of fabrics, but the error rate was high [8]. In recent years, the co-occurrence matrix was utilized extensively [9, 10]. [PERSON] et al. employed the gray-level co-occurrence matrix method for the identification of textures in images [11]. [PERSON] et al. applied the gray level co-occurrence matrix method for the analysis of the fabric textures [12]. However, poor judgment of warp floats and weft floats leads to an inability to identify fabric texture or, in other words, it leads to high errors. Although, these methods perform well over uniform (simple) or repetitive weave pattern fabric images, in thecase of complex weave patterns, the use of these methods for the fabric texture recognition involves more complex computations and also leads to higher classification error. The challenging difficulty of recognizing fabric textures in images is due to the changes in view-points, light intensity, shift and scale transforms, and occlusions. Meanwhile, the difficulty of pattern recognition lies in capturing the variance of shape and appearance of different fabric weave patterns that belongs to the same class label, while avoiding confusing weave patterns from different class labels. The trade-off between selectivity and invariance is a major challenge. Therefore, it is vital to develop a fabric texture recognition system with improved texture (complex patterns) recognition. Color is also an important property, yet remained unutilized to recognize patterns in most of the approaches. Recently, the use of color images has become a hot research topic. A number of researchers have made different attempts to extract color features in different ways, and the combination of color features with the object shape or texture features (in gray scale) has significantly improved the recognition performance in many recent studies [13; 14; 15]. The extensive use of the color information in the primary visual cortex is well known [16]. Although the extraction and utilization of color features for pattern recognition has become a hot research topic, yet most of the developed algorithms for fabric weave pattern recognition are still based on black and white images or gray scale images. The prime focus of our study is to perform fabric color feature extraction inspired by the primate visual cortex [17]. Biologically-plausible algorithms may prove to be a good alternative method for the classification of the fabric texture and yarn color features. The main reason behind this motivation is that the human vision system is the most powerful, intelligent, and flexible system to date, which out-performs machine vision in terms of learning capability, as well as recognition and classification performance, in most cases. Therefore, it is quite obvious that human-like intelligence can only be achieved in machine vision by imitating the neurological findings which explain the mechanism of the processing of pattern and color features inside human brain. Furthermore, biologically-inspired algorithms can be further improved in the future by mimicking the newest neurological findings.