Carpets Color and Pattern Detection Based on Their Images

Sayedeh Marjaneh Hosseini, Ali Mohhamad-Djafari, Adel Mohammadpour, Sobhan Mohammadpour, Mohammad Nadi

Abstract: In these days of fast-paced business, accurate automatic color or pattern detection is a necessity for carpet retailers. Many well-known color detection algorithms have many shortcomings. Apart from the color itself, neighboring colors, style, and pattern also affects how humans perceive color. Most if not all, color detection algorithms do not take this into account. Furthermore, the algorithm needed should be invariant to changes in brightness, size, and contrast of the image. In a previous experiment, the accuracy of the algorithm was half of the human counterpart. Therefore, we propose a supervised approach to reduce detection errors. We used more than 37,000 images from a retailer’s database as the learning set to train a Convolutional Neural Network (CNN, or ConvNet) architecture.