PersonRank: Detecting Important People in Images


Wei-Hong Li, Benchao Li , Wei-Shi Zheng

Sun Yat-Sen University

{liweih3, libch3},

pipeline picture

Always, some individuals in images are more important/attractive than others in some events such as presentation, basketball game or speech. However, it is challenging to find important people among all individuals in images directly based on their spatial or appearance information due to the existence of diverse variations of pose, action, appearance of persons and various changes of occasions. We overcome this difficulty by constructing a multiple Hyper-Interaction Graph to treat each individual in an image as a node and inferring the most active node referring to interactions estimated by various types of clews. We model pairwise interactions between persons as the edge message communicated between nodes, resulting in a bidirectional pairwise-interaction graph. To enrich the personperson interaction estimation, we further introduce a unidirectional hyper-interaction graph that models the consensus of interaction between a focal person and any person in a local region around. Finally, we modify the PageRank algorithm to infer the activeness of persons on the multiple Hybrid-Interaction Graph (HIG), the union of the pairwise-interaction and hyperinteraction graphs, and we call our algorithm the PersonRank. In order to provide publicable datasets for evaluation, we have contributed a new dataset called Multi-scene Important People Image Dataset and gathered a NCAA Basketball Image Dataset from sports game sequences. We have demonstrated that the proposed PersonRank outperforms related methods clearly and substantially.


Paper  [bibtex] 


▸ Multi-scene Important People Image Dataset [download] (Both datasets are provided for academic use only. Once you download the dataset, you guarantee that you will use this dataset for academic work only.)


▸ NCAA Basketball Image Dataset [download]


▸ Results

▸ CMC Curve

▸ Visualization Results