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The ecological footprint (EF) model is an effective tool for determining whether natural assets are over-utilized. The traditional EF (TEF) model and its improved model which include the emergy ecological footprint (EEF) and net primary productivity ecological footprint (NPPEF) have been widely used, but some emergy data are hard to obtain and NPP data is not stable. Therefore, in this paper, a novel three-dimensional (3D) EF model with emergy and net primary productivity (3DEF-ENPP) is proposed. The Henan province of China was chosen as the research area and commonly used statistical yearbook data and NPP data were used which are easy to obtain. We expanded a 2D EF model to a 3D EF model and took advantage of emergy analysis and net primary productivity because they have stable energy parameters, can reflect the difference in bioproductivity of different land types, and are suitable for spatial and temporal analysis. Based on our model, we obtained a rectified emergy-based ecological footprint (REEF), an ecological capacity based on net primary productivity (RNPPEC), a rectified ecological deficit (RED), an ecological footprint intensity (EFI), an ecological coordination coefficient, and a 3D-EF, which can comprehensively reflect Henan’s ecological security status. The results show that: (1) The REEF and RNPPED obtained by our proposed model are more stable than those of the former method. (2) Henan’s RED has been negative and has a downward trend, which indicates the burden of human activities on the natural environment are becoming increasingly serious. (3) The EF is increasing with time, indicating that the consumption of natural resources in Henan is gradually increasing. High EF regions are mainly distributed in the northwestern area. Southeastern regions have relatively low EFs. (4) Capital flows cannot meet the needs of current social development in Henan province and it is in a state of unsustainable development. (5) The ecological stress index is at a safe state but is still at an ecological security warning level and Henan has good ecological coordination.

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With the rapid growth of online videos, it is crucial to generate overviews of videos to help audiences make viewing decisions and save time. Video summarization and video captioning are two of the most common solutions. In this paper, we proposed a new solution in the form of a series of scene-person pairs generated from our proposed video description scheme. This new formation takes substantially less time than watching video summaries and is more acceptable than video captions. In addition, our method can be generalized to different types of videos. We also proposed a face clustering method and a scene detection method. The experimental results indicate that our methods outperform other state-of-the-art methods and are highly generalizable. As an example, a demo application is developed to demonstrate the proposed description scheme.