2025 Volume 13 Issue 4 Pages 372-381
This study proposes an effective handcrafted algorithm of product region extraction, which identifies product regions at image pixel level from images uploaded to consumer-to-consumer online marketplaces. The proposed method applies graph cuts to classify the image pixels and patches as either product or background regions. A comparative experiment using 425 product images demonstrated that the proposed method significantly improved the F-measure by at least 1.83% compared to a conventional method, with at least 0.13% and 2.16% of increase in precision and recall, respectively. In addition, the proposed method achieved the F-measure comparable to that of a supervised segmentation using deep neural networks, which were trained on 641 product images. Further comparisons and discussions in this paper provide a detailed evaluation of the effectiveness of the proposed method.
