ESUMM: Event SUMMarization on Scale-Free Networks
Krishan Kumar, Deepti D. Shrimankar · IETE Technical Review · 2018
In this digital era, events based video summarization has become an emerging topic in the field of computer vision which finds its applications in video data management, highlights of a game, video surveillance, etc. In this work, we propose a novel network-based approach for event summarization. First, the scale-free network is mapped to a neural network, and then dynamics of a complex video are determined by Chiavlo maps of the network. Second, we declare the neurons as hubs that are exceedingly well linked up as opposed to a majority who receive very short connections. Eventually, these hubs assume as the key-figures of the outcomes; then hubs and all its associated neurons counted for the issue summaries. Experiments show that the proposed approach outperforms the previous existing approaches on Precision and F-measure. The computing cost shows that the proposed model meets the criteria for real-time applications.