Related papers: IMAGO: A family photo album dataset for a socio-historical analysis of the twentieth century

IMAGO: A family photo album dataset for a socio-historical analysis of the twentieth century

URL: http://arxiv.org/abs/2012.01955v1
Date: Thu, 3 Dec 2020 14:28:58 GMT
Title: IMAGO: A family photo album dataset for a socio-historical analysis of the twentieth century
Authors: Lorenzo Stacchio, Alessia Angeli, Giuseppe Lisanti, Daniela Calanca, Gustavo Marfia
Abstract summary: We analyze the IMAGO dataset including photos belonging to family albums assembled at the University of Bologna's Rimini campus since 2004. Following a deep learning-based approach, the IMAGO dataset has offered the opportunity of experimenting with photos taken between year 1845 and year 2009.
Score: 4.54108183549264
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Although one of the most popular practices in photography since the end of the 19th century, an increase in scholarly interest in family photo albums dates back to the early 1980s. Such collections of photos may reveal sociological and historical insights regarding specific cultures and times. They are, however, in most cases scattered among private homes and only available on paper or photographic film, thus making their analysis by academics such as historians, social-cultural anthropologists and cultural theorists very cumbersome. In this paper, we analyze the IMAGO dataset including photos belonging to family albums assembled at the University of Bologna's Rimini campus since 2004. Following a deep learning-based approach, the IMAGO dataset has offered the opportunity of experimenting with photos taken between year 1845 and year 2009, with the goals of assessing the dates and the socio-historical contexts of the images, without use of any other sources of information. Exceeding our initial expectations, such analysis has revealed its merit not only in terms of the performance of the approach adopted in this work, but also in terms of the foreseeable implications and use for the benefit of socio-historical research. To the best of our knowledge, this is the first work that moves along this path in literature.

Related papers

The Role of Generative Systems in Historical Photography Management: A Case Study on Catalan Archives [0.24578723416255752]
The use of image analysis in automated photography management is an increasing trend in heritage institutions. The primary objective of this research is to study the quantitative contribution of generative systems in the description of historical sources. This is done by contextualizing the task of captioning historical photographs from the Catalan archives as a case study.
arXiv Detail & Related papers (2024-09-05T21:08:25Z)
Structuring Quantitative Image Analysis with Object Prominence [0.0]
We suggest carefully considering objects' prominence as an essential step in analyzing images as data. Our approach combines qualitative analyses with the scalability of quantitative approaches.
arXiv Detail & Related papers (2024-08-30T19:05:28Z)
Blind Dates: Examining the Expression of Temporality in Historical Photographs [57.07335632641355]
We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. We use the textitDe Boer Scene Detection dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. Our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers.
arXiv Detail & Related papers (2023-10-10T13:51:24Z)
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History [0.6345523830122167]
We introduce the first openly licensed data set for estimating human poses in art. The Poses of People in Art data set consists of 2,454 images from 22 art-historical depiction styles. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints.
arXiv Detail & Related papers (2023-01-12T16:23:58Z)
DEArt: Dataset of European Art [0.17188280334580194]
We propose DEArt, an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.
arXiv Detail & Related papers (2022-11-02T16:05:35Z)
Bugs in the Data: How ImageNet Misrepresents Biodiversity [98.98950914663813]
We analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases.
arXiv Detail & Related papers (2022-08-24T17:55:48Z)
There is a Time and Place for Reasoning Beyond the Image [63.96498435923328]
Images often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. We introduce TARA: a dataset with 16k images with their associated news, time and location automatically extracted from New York Times (NYT), and an additional 61k examples as distant supervision from WIT. We show that there exists a 70% gap between a state-of-the-art joint model and human performance, which is slightly filled by our proposed model that uses segment-wise reasoning, motivating higher-level vision-language joint models that
arXiv Detail & Related papers (2022-03-01T21:52:08Z)
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation [56.25878966006678]
We propose an approach of textbfPRactical textbfInference in textbfSocial rtextbfElation (PRISE) It concisely learns interactive features of persons and discriminative features of holistic scenes. PRISE achieves 6.8$%$ improvement for domain classification in PIPA dataset.
arXiv Detail & Related papers (2021-07-30T04:20:13Z)
Intentonomy: a Dataset and Study towards Human Intent Understanding [65.49299806821791]
We study the intent behind social media images with an aim to analyze how visual information can help the recognition of human intent. We introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding.
arXiv Detail & Related papers (2020-11-11T05:39:00Z)
Learning Patterns of Tourist Movement and Photography from Geotagged Photos at Archaeological Heritage Sites in Cuzco, Peru [73.52315464582637]
We build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit in Cuzco, Peru. Our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.
arXiv Detail & Related papers (2020-06-29T22:49:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.