کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535859 | 870396 | 2012 | 10 صفحه PDF | دانلود رایگان |
Together with the explosive growth of web video in sharing sites like YouTube, automatic topic discovery and visualization have become increasingly important in helping to organize and navigate such large-scale videos. Previous work dealt with the topic discovery and visualization problem separately, and did not take fully into account of the distinctive characteristics of multi-modality and sparsity in web video features. This paper tries to solve web video topic discovery problem with visualization under a single framework, and proposes a Star-structured K-partite Graph based co-clustering and ranking framework, which consists of three stages: (1) firstly, represent the web videos and their multi-model features (e.g., keyword, near-duplicate keyframe, near-duplicate aural frame, etc.) as a Star-structured K-partite Graph; (2) secondly, group videos and their features simultaneously into clusters (topics) and organize the generated clusters as a linked cluster network; (3) finally, rank each type of nodes in the linked cluster network by “popularity” and visualize them as a novel interface to let user interactively browse topics in multi-level scales. Experiments on a YouTube benchmark dataset demonstrate the flexibility and effectiveness of our proposed framework.
► We solve web video topic discovery problem with visualization under a single process.
► We model web videos and their features as a Star-structured K-partite Graph (SKG).
► The co-clustering of SKG is effective to group web videos into clusters (topics).
► The link cluster network ranking algorithm is helpful for web video visualization.
Journal: Pattern Recognition Letters - Volume 33, Issue 4, March 2012, Pages 410–419