![]() They combine different color descriptors to model the historical color film processes. suggest an approach to automatically estimate the age of historical color photos without restrictions to specific concepts. ![]() More closely related to our work, Palermo et al. Ginosar et al. and Salem et al. model the differences of human appearance and clothing style in order to predict the date of photos in yearbooks. apply convolutional neural networks in combination with optical character recognition. ![]() explore contour and stroke fragments, Li et al. address the task of estimating the age of historical documents. Jae et al. identify style-sensitive groups of patches for cars and street view images in order to model stylistic differences across time and space. The authors present an approach to sort a collection of city-scape images temporally by reconstructing the 3D world, requiring many overlapping images of the same location. The first work that deals with dating historical images stemming from different decades has been introduced by Schindler et al. 4 along with a comparison to human annotation performance. The experimental setup and results are presented in Sect. Section 3 introduces the Date Estimation in the Wild dataset as well as the baseline approaches in detail. Section 2 reviews related work on dating historical images. The remainder of the paper is organized as follows. Experimental results show the feasibility of the suggested approaches which are superior to annotations of untrained humans. Two baseline approaches are proposed based on a deep convolutional neural network (GoogLeNet ) treating the task of dating images as a classification and regression problem, respectively. 1, the dataset covers a broad range of domains, e.g., city scenes, family photos, nature, and historical events. In contrast to existing datasets, it contains more than one million Flickr images captured in the period from 1930 to 1999. In this paper, we introduce a novel dataset Date Estimation in the Wild and make it publicly available to support further research. For this reason, a huge dataset covering all kinds of concepts is necessary, which additionally enables the training of convolutional neural networks. Existing approaches either rely on datasets solely containing historical color images or focus on specific concepts like cities, cars, persons, or historical documents and are therefore unable to learn the temporal differences of the broad variety of motives. But date estimation is an interesting and challenging task for historians, archivists, and even for sorting (digitized) personal photo collections chronologically. However, estimating automatically the capturing time of (historical) photos has been rarely addressed yet and existing benchmark datasets do not contain enough images captured before 2000. In particular, such datasets are a prerequisite for the training of deep learning systems. In recent years, huge datasets (e.g., ImageNet , YFCC100M ) were introduced fostering research for many computer vision tasks. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. ![]() Experimental results demonstrate that these baselines are already superior to annotations of untrained humans. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. The problem of automatically estimating the creation date of photos has been addressed rarely in the past. ![]()
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