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.
Highlights
The wave of digitalization is taking over many business areas; carpet selling is no exception
We only consider a sample of 37,000 carpets from the last two years to speed up the classification process
We introduce three features: color, pattern, and style via frequency bar charts
Summary
The wave of digitalization is taking over many business areas; carpet selling is no exception. We apply a few selected image processing algorithms to e-CG’s database This e-CG is a production-level database with a reasonable error rate and contains fields like top-down image, pattern, and color (main color). Well-known and intuitive color detection method such as the pixel count of clustered image’s pixels fail due to the difference between a visual human color assessment of a carpet(or any other abstract image) and, e.g., counting the number of high-frequency pixels. This is due to the association between color and pattern in a carpet. We apply a few classification methods as well as CNN for pattern and color
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