Detecting Image Communities

2014-06-20
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.

Suggestions

Detecting User Emotions in Twitter through Collective Classification
İLERİ, İBRAHİM; Karagöz, Pınar (2016-11-11)
The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this s...
Identification of ligand binding regions of the Saccharomyces cerevisiae alpha-factor pheromone receptor by photoaffinity cross-linking
Son, Çağdaş Devrim; Naider, F; Becker, JM (American Chemical Society (ACS), 2004-10-19)
Analogues of alpha-factor, Saccharomyces cerevisiae tridecapeptide mating pheromone (H-Trp-His-Trp-Leu-Gln-Leu-Lys-Pro-Gly-Gln-Pro-Met-Tyr-OH), containing p-benzoylphenylalanine (Bpa), a photoactivatable group, and biotin as a tag, were synthesized using solid-phase methodologies on a p-benzyloxybenzyl alcohol polystyrene resin. Bpa was inserted at positions 1, 3, 5, 8, and 13 of alpha-factor to generate a set of cross-linkable analogues spanning the pheromone. The biological activity (growth arrest assay) ...
Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks
Aktunç, Rıza; Toroslu, İsmail Hakkı; Karagöz, Pınar (Springer, Cham, 2020-01-01)
In this work, we focus on social interactions in communities in order to detect events. There are several previous efforts for the event detection problem based on analyzing the change in the network structure in terms of the overall network features. However, in this work, event detection is considered as a problem of change detection in community structures. Particularly, communities extracted from communication network are focused on, and various versions of the community change detection methods are dev...
A parylene-based dual channel micro-electrophoresis system for rapid mutation detection via heteroduplex analysis
SUKAS, Sertan; Erson Bensan, Ayşe Elif; Sert, Cüneyt; Külah, Haluk (Wiley, 2008-09-01)
A new dual channel micro-electrophoresis system for rapid mutation detection based on heteroduplex analysis was designed and implemented. Mutation detection was successfully achieved in a total separation length of 250 pm in less than 3 min for a 590 by DNA sample harboring a 3 bp mutation causing an amino acid change. Parylene-C was used as the structural material for fabricating the micro-channels as it provides conformal deposition, transparency, biocompatibility, and low background fluorescence without ...
Comparison of tissue disease specific integrated networks using directed graphlet signatures
SÖNMEZ, ARZU BURÇAK; Can, Tolga (2016-10-05)
We present a novel framework for counting small sub-graph patterns in integrated genome-scale networks. An integrated network was built using the physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The network was filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. In this study, we apply and extend the idea of graphlet counting in...
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: https://hdl.handle.net/11511/68194.