This is one of the most respected books on film making ever published. With the aid of hundreds of photographs and diagrams, it clearly and concisely presents the essential concepts and techniques of motion picture camerawork and the allied areas of film making that they interact and impact with. Branching out from five central subject areas, the five Cs -- camera angles, continuity, cutting, close-ups, composition -- Mascelli offers film makers a detailed and practical course in visual thinking. Included are discussions of: cinematic time and space; compositional rules; point-of-view; camera height and angle; master scenes; types of editing; screen direction.

The five C's of cinematography: motion picture filming techniques simplified [lima jurus sinematografi]

This paper addresses the problem of generating meaningful summaries from unedited user videos. A framework based on spatiotemporal and high-level features is proposed in this paper to detect the key-shots after segmenting the videos into shots based on motion magnitude. To encode the time-varying characteristics of a video, we explore the local phase quantization feature descriptor from three orthogonal planes (LPQ-TOP). The sparse autoencoder (SAE), an instance of deep learning strategy, is used for the extraction of high-level features from LPQ-TOP descriptors to represent the shots carrying key-contents of videos efficiently. The Chebyshev distance between the feature vectors of the consecutive shots are calculated and thresholded using the mean value of the distance score as the threshold value. The optimal subset of shots with distance score greater than the threshold value is used to generate a high-quality video summary. The method is evaluated using SumMe data set. The summaries thus generated are of better quality than those produced by the other state of the art techniques. The effectiveness of the method is further evaluated by comparing with the human-created summaries in the ground truth.

Dynamic Summarization of Videos Based on Descriptors in Space-Time Video Volumes and Sparse Autoencoder

Video summarization is an important technique to help us browse, store, and retrieve a rapidly increasing amount of video data, which extracts frames or shots from the original video. Text information covers important content of a video, and thus a summarization can be generated by exploring the correlation between the frame and text. In this study, we propose a video summarization method based on the modality correlation. With this method, we first learn the correlation between the text and frame in the respective space, and then fuse two correlations to obtain the importance score of each shot. Finally, video shots that have a high importance score are chosen as the video summarization. Compared to previous methods that seldom apply text to generate the video summarization, or only use the latent common information between text and frame, the proposed method fully utilizes not only the latent common but also modality-specific information for a video summarization. Experiments were conducted on the TVSum50 dataset, and the results verify the effectiveness of our proposed approach.

Modality correlation-based video summarization

Video Summarization is to generate an important or interesting short video from a long video. It is important to reduce the time required to analyze the same archived video by removing unnecessary video data. This work proposes a novel method to generate dynamic video summarization by fusing the global importance and local importance based on multiple features and image quality. First, videos are split into several suitable video clips. Second, video frames are extracted from each video clip, and the center parts of frames are also extracted. Third, for each frame and the center part, the global importance and the local importance are calculated by using a set of features and image quality. Finally, the global importance and the local importance are fused to select an optimal subset for generating video summarization. Extensive experiments are conducted to demonstrate that the proposed method enables to generate high-quality video summarization.

Video summarization via exploring the global and local importance

Video summarisation greatly improves the efficiency of people browsing videos and saves storage space. A good video summary should satisfy human visual interestingness and preserve the theme of the original video at the semantic level. Unlike many existing methods that consider only visual features to generate video summaries, this study proposes a method that combines visual and semantic cues to extract important information for dynamic video summarisation. The authors propose visual-verbal saliency consistency to add semantic information and propose a novel attention motion, along with other visual features to fully represent visual interestingness. Based on the importance score of each frame calculated by combining these features, they select an optimal subset of segments to generate an important and interesting summary. They evaluate their method using the SumMe and TVSum datasets and experimental results show that their method generates high-quality video summaries.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1355

Video summarisation with visual and semantic cues

It is important to generate both interesting and representative video summary for massive videos. This work proposes a new method to generate dynamic video summary using multiple features and image quality without human's involvement in the whole procedure. Specifically, we first split a video into several video clips. Second, a set of features including visual attention, exposure of light, saturation, hue, rule of thirds, contrast and directionality is computed and the qualities of video clips are also estimated. Then, the importance of each video clip is obtained based on these features and the estimated quality. Finally, based on the importance value, we sort clip values in descending order and select an optimal subset to generate video summary. Experimental results demonstrate that the proposed method enables to generate high-quality video summary.

Xing Mu

Papers

Unsupervised Video Summaries Using Multiple Features and Image Quality

Learning transformer-based attention region with multiple scales for occluded person re-identification