Automatic product region extraction based on colour similarity and saliency detection models

In this paper, product region extraction, which can classify the pixels of the product images as product and background regions, is proposed. The proposed method is based on the handcrafted algorithm using both the colour similarity and the saliency detection. Our experiment, which employed 180 product images, clarified that the proposed method increased all the metric for the extraction accuracy compared with conventional methods based on the handcrafted algorithm. The F-measure, which is the comprehensive metric, was significantly increased by 2.20% or more. Our discussion also found that the proposed method also overcame the shortcoming of the conventional method, because the F-measure for the dataset, the accuracy of which was decreased by the conventional method, was significantly improved. In addition, the F-measure was increased by 0.92% or more for each product category. Further comparison and discussion are included in this paper to provide more focused findings.