SODRet: Instance retrieval using salient object detection for self-service shopping

Self-service shopping technologies have become commonplace in modern society. Although various innovative solutions have been adopted, there is still a gap in providing efficient services to consumers. Recent developments in mobile application technologies and internet-of-things devices promote information and knowledge dissemination by integrating innovative services to meet users’ needs. We argue that object retrieval applications can be used to provide effective online or self-service shopping. Therefore, to fill this technological void, this study aims to propose an object retrieval system using a fusion-based salient object detection (SOD) method. The SOD has attracted significant attention, and recently many heuristic computational models have been developed for object detection. It has been widely used in object detection and retrieval applications. This work proposes an instance retrieval system based on the SOD to find the objects from the commodity datasets. A prediction about the object’s position is made using the saliency detection system through a saliency model, and the proposed SOD-based retrieval (SODRet) framework uses saliency maps for retrieving the searched items. The method proposed in this work is evaluated on INSTRE and Flickr32 datasets. Our proposed work outperforms state-of-the-art object retrieval methods and can further be employed for large-scale self-service shopping-based points of sales.