Graph-based modelling of superpixels for automatic identification of empty shelves in supermarkets
作者：
Highlights：
• A graph-based model of superpixels is introduced for segmenting gap/non-gap regions in the shelf images of supermarkets.
• A shelf image is over-segmented into superpixels to create a graph of superpixels (SG).
• The nodes and edges of a SG are uniquely encoded using our graph convolutional and Siamese networks.
• A structural support vector machine is formulated with the SG for finding gaps in shelves.
• Our annotations for three retail product datasets labelling gap/non-gap regions are released at https://github.com/gapDetection/gapDetectionDatasets.
摘要
•A graph-based model of superpixels is introduced for segmenting gap/non-gap regions in the shelf images of supermarkets.•A shelf image is over-segmented into superpixels to create a graph of superpixels (SG).•The nodes and edges of a SG are uniquely encoded using our graph convolutional and Siamese networks.•A structural support vector machine is formulated with the SG for finding gaps in shelves.•Our annotations for three retail product datasets labelling gap/non-gap regions are released at https://github.com/gapDetection/gapDetectionDatasets.
论文关键词：Gap detection,Retail store,Graph convolutional network,Siamese network,Structural support vector machine
论文评审过程：Received 24 June 2021, Revised 17 February 2022, Accepted 5 March 2022, Available online 6 March 2022, Version of Record 19 March 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108627
