Abstract
We propose a few fundamental techniques to obtain effective watermark features of images in the image search index, and utilize the signals in a commercial search engine to improve the image search quality. We collect a diverse and large set (about 1M) of images with human labels indicating whether the image contains visible watermark. We train a few deep convolutional neural networks to extract watermark information from the raw images. We also analyze the images based on their domains to get watermark information from a domain-based watermark classifier. The deep CNN classifiers we trained can achieve high accuracy on the watermark data set. We demonstrate that using these signals in Bing image search ranker, powered by LambdaMART, can effectively reduce the watermark rate during the online image ranking.
Abstract (translated by Google)
URL
http://arxiv.org/abs/1901.09473