Title :
Extracting deep social relationships from photos
Author :
Yelei Lu ; Aarabi, P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
Abstract :
Hidden within the relative location of tags in images is a relational model that can identify how close two individuals are, or, the affinity of a person to an object or a brand. Based on this model we can 1) better understand the relationship between users/tags, 2) find photos where a user is pictured but not tagged in, and 3) enable searching “inside” images by clicking on any location within an image to start a search. This paper proposes a method of modeling the relationship between objects based on their spatial arrangement in a set of tagged images. Based on the relative coordinates of each object tag, we compute a joint relativity between each tag pair, generate a social relationship graph and propose an efficient image search method using the joint Relativity graph. We evaluated our approach with real world data from Facebook, showing a direct relationship between the number of tagged photos and the amount of information obtained from these photos, and an average correlation coefficient of 0.8 between user-generated relativity scores and those obtained by our algorithm.
Keywords :
correlation methods; digital photography; graph theory; image retrieval; social networking (online); Facebook; average correlation coefficient; deep social relationships extraction; image search method; images tags; joint relativity graph; object tag; relational model; social networks; social relationship graph; spatial arrangement; tagged photos; user-generated relativity scores; Computational modeling; Correlation coefficient; Data mining; Facebook; Method of moments; Predictive models; link prediction; social networks; social relationship modeling and extraction;
Conference_Titel :
Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
Conference_Location :
San Diego, CA
DOI :
10.1109/SMC.2014.6973949
