Detecting Image Communities

Esen, Ersin
Ozkan, Savas
Atil, Ilkay
Arabaci, Mehmet Ali
Tankiz, Seda
In this work, we propose a novel community detection method that is specifically designed for image communities. We define image community as a coherent subgroup of images within a large set of images. In order to detect image communities, we construct an image graph by utilizing visual affinity between each image pair and then prune most of the links. Instead of affinity values, we prefer ranking of neighboring images and get rid of range mismatch of affinity values. The resulting directed graph is processed to detect the image communities by using the proposed deterministic method. The proposed method is compared against state-of-the-art community detection methods that can operate on directed graphs. In the experiments, we use various sets of images for which ground truths are determined manually. The results indicate that our method significantly outperforms the compared state-of-the-art methods. Furthermore, the proposed method appears to have a consistent performance between sets unlike the compared methods. We believe that the proposed community detection method can be successfully utilized in many different applications.
Citation Formats
E. Esen, S. Ozkan, I. Atil, M. A. Arabaci, and S. Tankiz, “Detecting Image Communities,” 2014, p. 0, Accessed: 00, 2020. [Online]. Available: