Detecting and outlining products in images is beneficial for many use cases in e-commerce, such as automatically identifying and locating products within images and proposing matches for the detections. This study investigated how the utilisation of metadata associated with images of products could help boost the performance of an existing approach with the ultimate goal of reducing manual labour needed to annotate images. This thesis explored if approximate pseudo masks could be generated for products in images by leveraging metadata as image-level labels and subsequently using the masks to train a Mask R-CNN. However, this approach did not result in satisfactory results. Further, this study found that by incorporating the metadata directly in the Mask R-CNN, an mAP performance increase of nearly 5\% was achieved. Furthermore, utilising the available metadata to divide the training samples for a KNN model into subsets resulted in an increased top-3 accuracy of up to 16\%. By representing the data with embeddings created by a pre-trained CNN, the KNN model performed better with both higher accuracy and more reasonable suggestions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-176941 |
| Date | January 2021 |
| Creators | Wahlquist, Gustav |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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