Understanding the cultural concerns of libraries based on automatic image analysis

Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections.,In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns.,The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives.,To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.

[1]  David L. Jacobs Domestic Snapshots: Toward a Grammar of Motives , 1981 .

[2]  Marija Dalbello,et al.  A genealogy of digital humanities , 2011, J. Documentation.

[3]  Jennifer Rowley,et al.  Cultural sustainability as a strategy for the survival of museums and libraries , 2017, Cultural Policies for Sustainable Development.

[4]  Jian Wang,et al.  Cross-Cultural Communication: Implications for Effective Information Services In Academic Libraries , 2002 .

[5]  Erez Lieberman Aiden,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010, Science.

[6]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  George Buchanan,et al.  An examination of the physical and the digital qualities of humanities research , 2008, Inf. Process. Manag..

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  G. Hofstede Cultural dimensions in management and planning , 1984 .

[10]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[11]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yann LeCun,et al.  A Closer Look at Spatiotemporal Convolutions for Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[15]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  Russell W. Belk,et al.  Cross-cultural differences in materialism , 1996 .

[17]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  John P. Eakins,et al.  Towards intelligent image retrieval , 2002, Pattern Recognit..

[19]  Mubarak Shah,et al.  UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.

[20]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Martin R. Kalfatovic,et al.  Smithsonian Team Flickr: a library, archives, and museums collaboration in web 2.0 space , 2008 .

[22]  Arnim Bleier,et al.  When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter , 2014, ICWSM.

[23]  Furio Camillo,et al.  Semiometric Approach, Qualitative Research and Text Mining Techniques for Modelling the Material Culture of Happiness , 2005 .

[24]  M. Stuart-Fox,et al.  The unit of replication in socio-cultural evolution , 1986 .

[25]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  S. Schwartz A Theory Of Cultural Value Orientations: Explication And Applications , 2006 .

[27]  Rebecca S. Guenther,et al.  Practical Preservation: The PREMIS Experience , 2005, Libr. Trends.

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Kathleen M. Carley A Theory of Group Stability , 1991 .

[30]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.