During recent years, deep learning methods have shown to be effective for image classification, localization and detection. Convolutional Neural Networks (CNN) are used to extract information from images and are the main element of modern machine learning and computer vision methods. CNNs can be used for logo detection and recognition. Logo detection consist on locate and recognize commercial brand logos within an image. These methods are useful in the areas of online brand management or ad placement. The performance of this methods is closely related on the quantity and the quality of the data, typically image/label pairs, used to train the CNNs. Collecting the pair of images and labels, commonly referred as ground truth, can be expensive and time consuming. Multiple techniques try to solve this problem by either transforming the available data using data augmentation methods or by creating new images from scratch or from other images using image synthesis methods. In this paper, we investigate the latter approach. We segment background images, extract depth information and then blend logo images accordingly in order to create new real looking images. This approach allows us to create an indefinite number of images with a minimum manual labeling effort. The synthetic images can later be used to train CNNs for logo detection and recognition.
Daniel Mas Montserrat, Qian Lin, Jan Allebach, Edward J. Delp, "Logo detection and recognition with synthetic images" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 337-1 - 337-7, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-337