Which Region Proposal to Choose? A Case Study for Automatic Identification of Retail Products

Identifying products visible in an image of a rack of a supermarket is a challenging and commercially relevant machine vision problem. For identification, the region proposal algorithm generates a number of (mostly overlapped) region proposals around each product on the rack. Each region proposal is then assigned a product class with a certain classification score. Finally, the products are detected using non-maximal suppression (NMS) discovering winners among the region proposals. Greedy-NMS takes classification scores of the proposals as a key factor and thereby often eliminates (geometrically) better-fitted proposals. Graph-based NMS (G-NMS) provides a better alternative performance-wise but an inferior solution timecomplexity-wise (O(N3)) compared to O(N2) time-complexity of greedy-NMS. This paper introduces a adjusted classification score for use in the novel rectified non-maximal suppression (RNMS) setup that runs in O(N2). The efficacy of the proposed adjusted classification score is theoretically characterized in better discriminating overlapped region proposals. Our experiments establish that the performance of the proposed R-NMS is never inferior to G-NMS, and outperforms greedy-NMS while testing on several datasets.