Final Key Frames Research Articles

Keyframe Generation Method via Improved Clustering and Silhouette Coefficient for Video Summarization

In order to solve the issue that the traditional k-means algorithm falls into the local optimal solution in video summarization due to unreasonable initial parameter setting, a video summarization generation algorithm by using improved clustering and silhouette coefficient was proposed. Firstly, color features and texture features are extracted and fused from the decomposed video frames. Secondly, the hierarchical clustering algorithm is used to obtain the initial clustering results. And then, the improved k-means algorithm with silhouette coefficient is introduced to optimize the initial clustering results. Finally, the nearest frame from the cluster center is selected as the keyframe, and all the final keyframes are arranged in the order of the time sequence in the original video to constitute video summarization. The proposed algorithm is evaluated on the benchmark Open Video Database dataset with an average 71% precision, 84% recall rate, and 76% F-score, which is higher than state-of-the-art video summarization methods. Moreover, it generates video keyframes that are closer to user summaries, and it improves effectively the overall quality of the generated summary.

Video Summarization for Copyright Protection

Nowadays, a summarization of videos for copyright application is an interested research area in computer vision. Key frames extraction is crucial phase in video summarization. This work proposed a method for key frames extraction. The method consists of two operations for covering the content of the whole video sequence. Firstly, a set of candidates key frames are extracted based on adaptive thresholding of difference of histogram of adjacent frames of video data. Secondly, the extraction of a final key frames is done depending on computing Euclidian distance between two adjacent frames in candidates key frames set. The proposed method was tested with a set of standard and online television videos. The experimental results show the efficiency of the proposed system.

Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map

Social networking tools such as Facebook, YouTube, Twitter, and Instagram, are becoming major platforms for communication. YouTube as one of the primary video sharing platform serves over 100 million distinct videos, 300 hours of videos are uploaded on YouTube every minute along with textual data. This massive amount of multimedia data needs to be managed with high efficiency, the irrelevant and redundant data needs to be removed. Video summarization ideals with the problem of redundant data in a video. A summarized video contains the most distinct frames which are termed as key frames. Most of the research work on key frames extraction considers only a single visual feature which is not sufficient for capturing the full pictorial details and hence affecting the quality of video summary generated. So there is a need to explore multiple visual features for key frames extraction. In this research work a key frame extraction technique based upon fusion of four visual features namely: correlation of RGB color channels, color histogram, mutual information and moments of inertia is proposed. Kohonen Self Organizing map as a clustering approach is used to find the most representative frames from the list of frames coming after fusion. Useless frames are discarded and frames having maximum Euclidean distance within a cluster are selected as final key frames. The results of the proposed technique are compared with the existing video summarization techniques: User generated summary, Video SUMMarization (VSUMM), and Video Key Frame Extraction through Dynamic Delaunay Clustering (VKEDDCSC) which shows a considerable improvement in terms of fidelity and Shot Reconstruction Degree (SRD) score.

AN IMPROVED ACTION KEY FRAMES EXTRACTION ALGORITHM FOR COMPLEX COLOUR VIDEO SHOT SUMMARIZATION

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems

A Clustering Algorithm for Key Frame Extraction Based on Density Peak

Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensional data, and reduces the computational complexity while capturing image features. On this basis, the density peak clustering algorithm is used to cluster these low-dimensional data and find the cluster centers. Combining the clustering results, the final key frames are obtained. A large number of key frame extraction experiments for different types of videos show that the algorithm can extract different number of key frames by combining video content, overcome the shortcoming of traditional key frame extraction algorithm which can only extract a fixed number of key frames, and the extracted key frames can represent the main content of video accurately.

HYBRID APPROACH FOR KEY FRAME EXTRACTION FROM VIDEO SEQUENCE

This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.

A Fast and Robust Key Frame Extraction Method for Video Copyright Protection

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.

New fusional framework combining sparse selection and clustering for key frame extraction

Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information‐based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content‐loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post‐processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods.

Parallel Key Frame Extraction for Surveillance Video Service in a Smart City.

Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surveillance video applications, key frames are typically used to summarize important video content. It is very important and essential to extract key frames accurately and efficiently. A novel approach is proposed to extract key frames from traffic surveillance videos based on GPU (graphics processing units) to ensure high efficiency and accuracy. For the determination of key frames, motion is a more salient feature in presenting actions or events, especially in surveillance videos. The motion feature is extracted in GPU to reduce running time. It is also smoothed to reduce noise, and the frames with local maxima of motion information are selected as the final key frames. The experimental results show that this approach can extract key frames more accurately and efficiently compared with several other methods.

Motion Key-Frames Extraction Based on Locally Linear Embedding

A motion key-frames extraction algorithm based on LLE was presented. First, the dimensionality of motion capture data was reduced by LLE method to obtain the low dimension feature curve. Second, the initial key-frames were obtained by extracting the local extreme points. In the end, according to the difference of feature curve amplitude and the threshold, the corresponding key-frames were inserted to get the final key-frames. A amount of experiments showed that the algorithm could generalize the content of original motion sequence on the basis of the compression ratio and low error rate.

Motion Key-frames extraction based on amplitude of distance characteristic curve

AbstractThe key frames extraction technique extracts key postures to describe the original motion sequence, which has been widely used in motion compression, motion retrieval, motion edition and so on. In this paper, we propose a method based on the amplitude of curve to find key frames in a motion captured sequence. First we select a group of joint distance features to represent the motion and adopt the Principal Component Analysis (PCA) method to obtain the one dimension principal component as a features curve which will be used. Then we gain the initial key-frames by extracting the local optimum points in the curve. At last, we get the final key frames by inserting frames based on the amplitude of the curve and merging key frames too close. A number of experimental examples demonstrate that our method is practicable and efficient not only in the visual performance but also in the aspect of the compression ratio and error rate.

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Wireless capsule endoscopy (WCE) is a non-invasive imaging technique to examine the entire small intestine. This paper presents a novel hierarchical key frame extraction algorithm based on the saliency map to automatically select a small number of key informative frames in the WCE video to assist the diagnostic process. We start from calculating the saliency map and extracting the inside colour, texture and shape features. We then compute the image information entropies with adapted weights on three features; calculate the differences of entropies in the successive frames and extract the first level key frames to divide the video into multi-segments. Finally, the mean shift clustering method is applied on each segment to obtain the final key frames. The proposed technique achieves the fidelity of 95.07% and the compression ratio of 87.9% on average, validating that the proposed scheme is highly promising for the key frame extraction in the WCE video.