An Improved Audio Based Key Frame Detection Model for Movie Summarization

In this paper, we demonstrate a system for the key frame summarization, extraction, and browsing of the echocardiogram videos. Echocardiograms are video sequences produced by the ultrasound scanning of the heart. Echocardiography is the most common type of medical imaging modality in the field of cardiology. Our approach combines the domain-specific knowledge and the automatic analysis of the spatio-temporal structure of the echocardiogram videos. The videos are temporally sampled using the embedded electrocardiogram graph (ECG). The consecutive sampled frames are compared based on the shape of the region of interest to detect the boundaries between the different segments. The content of each segment of the video is summarized into two forms: the static and the dynamic summaries. Finally the summary is displayed in the user interface in an intuitive form for the purpose of browsing.

Key Frame Extraction Techniques: A Survey

Advancements in technology resulted in a sharp growth in the number of digital cameras at people's disposal all across the world. Consequently, the huge storage space consumed by the videos from these devices on video repositories make the job of video processing and analysis to be time-consuming. Furthermore, this also slows down the video browsing and retrieval. Video summarization plays a very crucial role in solving these issues. Despite the number of video summarization approaches proposed up to the present time, the goal is to take a long video and generate a video summary in form of a short video skim without losing the meaning or the message transmitted by the original lengthy video. This is done by selecting the important frames called key-frames. The approach proposed by this work performs automatic summarization of digital videos based on detected objects' deep features. To this end, we apply sparse subspace clustering with an automatically estimated number of clusters to the objects' deep features. The summary generated from our scheme will store the meta-data for each short video inferred from the clustering results. In this paper, we also suggest a new video dataset for video summarization. We evaluate the performance of our work using the TVSum dataset and our video summarization dataset.

The purpose of video key frame extraction is to use as few video frames as possible to represent as much video content as possible, reduce redundant video frames, and reduce the amount of computation, so as to facilitate quick browsing, content summarization, indexing, and retrieval of videos. In this paper, a method of dance motion recognition and video key frame extraction based on multifeature fusion is designed to learn the complicated and changeable dancer motion recognition. Firstly, multiple features are fused, and then the similarity is measured. Then, the video sequences are clustered by the clustering algorithm according to the scene. Finally, the key frames are extracted according to the minimum amount of motion. Through the quantitative analysis and research of the simulation results of different models, it can be seen that the model proposed in this paper can show high performance and stability. The breakthrough of video clip retrieval technology is bound to effectively promote the inheritance and development of dance, which is of great theoretical significance and practical value.

An efficient video summarization for surveillance system using normalized k-means and quick sort method

The layup optimization of the laminated composites is a very complex topic which involves a convoluted solution space usually explored using heuristic computational techniques. Due to the solution space complexity a lot of layup configurations are evaluated during the optimization process. This fact leads to the mandatory requirement that the configuration evaluation should be fast enough to ensure the convergence of the optimization procedure without sacrificing the accuracy. In this work, we propose a robust, accurate and very fast finite element model based on the first-order shear deformation theory (FSDT). The model is structured in three main parts: preprocessing, processing and post processing. The main strategy is to transfer as much as possible operations in the preprocessing phase which is executed only once and to subsequently reuse the results in the processing and post processing phases which are executed for each layup configuration. Using this strategy, the execution time of the processing and post processing phases is drastically reduced and almost consists of regenerating and solving the global linear system - more that 95%. The proposed procedure is relatively easy to implement in Matlab which holds a very powerful linear system solver for sparse matrices. Also, the accuracy of the model was demonstrated by comparison with Ansys and with some closed form solutions.

In this paper, a new approach for key frame extraction for sports video summarization is presented. Our approach employs dominant value of shot volume and energy. For the dominant value of each shot audio energy corresponding video key frame is extracted. This key frame of each shot serves as accurate localization of the events on the field. This is applied to cricket, soccer and lawn tennis videos and the experimental results show our proposed method is promising. Abstract In this paper, a new video summarization approach for lawn tennis video is presented. The proposed method uses frame color histogram to classify video into play field color shots (PFCS) and non play field color shots (NPFCS). Play field color shots are the segments of interest and used to recognize the tournament class. A dominant colored frame from every PFCS is extracted as a salient frame. Our approach also employs dominant value of PFCS volume and energy. For the each PFCS dominant audio energy value is computed and corresponding key frame is extracted. On-screen text presented in play field color shots provides important semantic information so the last frame of the shot is extracted as key caption. Three key frames based on audio, visual and text information conveyed in every PFCS are extracted and summary of lawn tennis video is created. These key frames of every PFCS serve as accurate localization of the events on the lawn tennis field. Experiments are performed on lawn tennis videos to confirm the efficiency of the proposed method.

Towards genre-specific frameworks for video summarisation: A survey

The typical Online Handwritten Text Recognition System contains four main phases which are: preprocessing, feature extraction, recognition, and post-processing phases. Preprocessing phase aims to reduce or remove imperfections caused during acquisition step. This phase is also used to minimize handwriting variations irrelevant for pattern classification which may exist in the text acquisition. The preprocessing phase has a great influence on subsequent processing, and a real impact on the recognition rate. This phase could include a number of steps like resizing, centering, simplifying, and smoothing the text. This paper presents an experimental study of using a simplification technique which is used in the preprocessing phase in Online Handwritten Text Recognition systems. The proposed system is designed to deal with any type of text. However, the study is limited to deal with acquired digits which will give a deep understanding of using preprocessing steps in this field.

Depression screening with textual and audio features based on large language models and machine learning.

Short videos on the Internet are growing exponentially, and the number of videos uploaded every day is huge; people also involve a lot of video data in real life. People can retrieve and view all kinds of videos, but it also brings a lot of problems. On the one hand, the accumulation of a large number of videos makes people unable to find the videos they want quickly, and the repeated scenes in the videos will also waste people's time and energy; on the other hand, a large amount of video data also brings enormous pressure to storage. Aiming at the problems of inaccurate selection of key frames and how to select video frame features in existing video summarization models, this paper proposes a multi-feature-based video summarization generation model (DME-VSNet), which extracts multiple features of video frames. Including importance score, image memory strength and image entropy. Aiming at the problem of inaccurate video shot segmentation, this model proposes a video shot segmentation algorithm based on TransNet network, which divides the original video into several short shots through shot boundaries; the model inputs the above three features into the proposed The video frame score is obtained in the MLP architecture, and the key frame is selected by the score to generate a video summary. The effectiveness of the video shot segmentation method based on TransNet network and the overall model based on convolutional neural network is verified by comparative experiments. The experimental results show that the evaluation results of the video summaries generated by the three features are better.

In the current era, most of the digital information is in the form of multimedia with a giant share of videos. Videos do have audio and visual content where the visual content has number of frames put in a sequence. Most of the consecutive frames do have very little discriminative contents. In video summarization process, several frames containing similar information do need to get processed. This leads to redundant slow processing speed and complexity, time consumption. Video summarization using key frames can ease the speed up of video processing. In this paper, novel key frames extraction method is proposed with Kekere's Proportionate Error (KPE) codebook generation techniques of vector quantization with ten different codebook sizes and two color spaces (RGB and KLUV). Experimentation done with help of the test bed of videos has shown that higher codebook sizes of KPE have given better completeness in key frame extraction for video summarization. The LUV color space with Euclidean Distance with 512 codebook size gives best performance. In square chord Distance, Mean Square Error and Euclidean Distance LUV color space gives better completeness than RGB color space for proposed KPE based video Key frame Extraction

Recently, a huge amount of the video data available in the digital form has given users to allow more ubiquitous access to visual information than ever. To efficiently manage such huge amount of video data, we need such tools as video summarization and search. In this paper, we propose a novel scheme allowing for both scalable hierarchical video summary and efficient retrieval by introducing a notion of fidelity. The notion of fidelity in the tree-structured key frame hierarchy describes how well the key frames at one level are represented by the parent key frame, relative to the other children of the parent. The experimental results demonstrate the feasibility of our scheme.

3D video, which consists of a sequence of mesh models, can reproduce dynamic scenes containing 3D information. To summarize 3D video, a key frame extraction method is developed using rate-distortion (R-D) trade-off. For this purpose, an effective feature vector is extracted for each frame. Shot detection is performed using the feature vectors as a preprocessing followed by key frame extraction. Simple but reasonable definitions of rate and distortion are presented. Based on an assumption of linearity, an R-D curve is generated in each shot, where the locations of the key frames are optimized. Finally, R-D trade-off can be achieved by optimizing a cost function using a Lagrange multiplier, where the number of key frames is optimized in each shot. Therefore, our system will automatically determine the best locations and the number of key frames in the sense of R-D trade-off. Our experimental results show the extracted key frames are compact and faithful to the original 3D video.

We present a new approach for video summarization and browsing of MPEG-2 compressed video based on the growing cell structures (GCS) neural algorithms. It first applies GCS to select keyframes for each shot and then clusters them using TreeGCS to form a hierarchical view of the video for efficient browsing. The keyframe selection is based on histogram features of the dc-images for T frames. It captures well the video content and outperforms two other approaches. The main advantage of the TreeGCS module is the ability to form dynamically a flexible hierarchy depending on the video content.