#Video montage movie#

As not relying on any user interaction information (e.g. To solve this problem, we propose a learning-based automatic GIF thumbnail generation model, which is called Generative Variational Dual-Encoder (GEVADEN). To support this study, we build the first GIF thumbnails benchmark dataset that consists of 1070 videos covering a total duration of 69.1 hours, and 5394 corresponding manually-annotated GIFs. Here, a GIF thumbnail is an animated GIF file consisting of multiple segments from the video, containing more information of the target video than a static image thumbnail. In this paper, we address a novel problem, namely GIF thumbnail generation, which aims to automatically generate GIF thumbnails for videos and consequently boost their Click-Through-Rate (CTR). And static image thumbnails contain very limited information of the corresponding videos, which prevents users from successfully clicking what they really want to view. However, current video thumbnails are created manually, which is time-consuming and quality-unguaranteed. While facing huge amounts of videos, a viewer clicks through a certain video with high probability because of its eye-catching thumbnail. Generally, these videos are presented on video streaming sites with image thumbnails and text titles. With the rapid increase of mobile devices and online media, more and more people prefer posting/viewing videos online. Furthermore, we report the usage of VideoModerator through a case scenario and conduct experiments and a controlled user study to validate its effectiveness. In the audio view, we employ a storyline-based design to provide a multi-faceted overview which can be used to explore audio content. In the frame view, we present a novel visual summarization method that combines risk-aware features and video context to enable quick video navigation. In the video view, we adopt a segmented timeline and highlight high-risk periods that may contain deviant information. Moreover, this framework introduces an interactive visualization interface with three views, namely, a video view, a frame view, and an audio view. This framework incorporates a set of advanced machine learning models to extract the risk-aware features from multimodal video content and discover potentially deviant videos. To ensure effective video moderation, we propose VideoModerator, a risk-aware framework that seamlessly integrates human knowledge with machine insights. However, this task is tedious and time consuming due to the difficulties associated with watching and reviewing multimodal video content, including video frames and audio clips. Video moderation, which refers to remove deviant or explicit content from e-commerce livestreams, has become prevalent owing to social and engaging features. The effecliveness of our method is validated by extensive experiments over a wide variety of videos. Since our method is uble to cut off spatially und temporally less informative portions, it is uble to generate much more compact yet highly informative output videos. To achieve the packing process, we develop a new algorithm based upon the first-fit and Graph cut optimization techniques.

The layers are then packrd together in a smull ouzput video volume such that the total amount of visual information in the video volume is maximized. The informative video porlions are represented in volumetric layers. The method simultaneously analyzes both the spatial and temporal injbrmation distribution in a video sequence, and extracts the visually informative space-time portions of the input videos.

In this paper we propose a novel spacetime video summarization method which we call space-time video montage.

#Video montage movie#

Conventional video summarization methods focus predominantly on summarizing videos along the time axis, such as building a movie trailer: The resulting video trailer tends to retain much empty space in the background of the video frames while discarding much informative video content due to size limit.