Name
Abstract | ||
|---|---|---|
Recent advances in the film industry have given rise to exponential growth in movie/drama production and adaptation of the Big Data concept. Automatic identification and classification of movie characters have received tremendous attention from researchers due to its applications in video semantic analysis, video summarization, and personalized video retrieval for which several methods have been recently presented. However, these methods cannot detect main characters properly due to their variation in pose and style in different scenes of a movie. To address this problem we present DeepStar, a novel framework for starring character identification based on deep high-level robust features. The proposed framework is threefold: the extraction of shots with clear faces from the input video; face clustering using discriminative deep features; and the occurrence matrix generation, helping in the selection of starring characters. The promising results obtained using representative Hollywood movies demonstrate the effectiveness of our method in detecting starring characters over the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2890560 | IEEE ACCESS |
Keywords | Field | DocType |
Big data,face detection,starring character identification,knowledge discovery,movie analysis,deep learning,clustering | Automatic summarization,Video retrieval,Information retrieval,Computer science,Film industry,Drama,Cluster analysis,Big data,Discriminative model,Hollywood,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 3 |
PageRank | References | Authors |
0.38 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ijaz Ul Haq | 1 | 7 | 2.77 |
| Khan Muhammad | 2 | 986 | 67.67 |
| Amin Ullah | 3 | 109 | 11.60 |
| Sung Wook Baik | 4 | 960 | 57.77 |