2021/10/07 by Harshita Seth, Seth, Harshita, Sonaal Kant +3
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Artificial neural network #Baseline (sea) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Engineering #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Handwritten Text Recognition Techniques #Machine Learning (cs.LG) #Machine learning #Margin (machine learning) #Matching (statistics) #Mathematics #Pipeline (software) #Point (geometry) #Point of sale #Stylized fact #Task (project management) #Window (computing) #World Wide Web #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.03646
arxiv created 2021/10/07 · openalex publication_date 2021/10/07 · arxiv updated 2021/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Point of Sale Materials(POSM) are the merchandising and decoration items that are used by companies to communicate product information and offers in retail stores. POSMs are part of companies' retail marketing strategy and are often applied as stylized window displays around retail shelves. In this work, we apply computer vision techniques to the task of verification of POSMs in supermarkets by telling if all desired components of window display are present in a shelf image. We use Convolutional Neural Network based unsupervised keypoint matching as a baseline to verify POSM components and propose a supervised Neural Network based method to enhance the accuracy of baseline by a large margin. We also show that the supervised pipeline is not restricted to the POSM material it is trained on and can generalize. We train and evaluate our model on a private dataset composed of retail shelf images.