Consumer video summarization based on image quality and representativeness measure

Jeong, Dong-ju; Yoo, Hyoung Jin; Cho, Nam Ik

doi:10.1109/globalsip.2015.7418260

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Kuanar S. K. computes the cluster threshold by employing formula 2(1 − ε) [8]. Jeong D. J. selects 0.0001 as the cluster threshold [10]. The other researchers compute cluster threshold by a self-defined formula [9,11].…”

Section: Related Workmentioning

confidence: 99%

“…Liu and his colleagues calculate the initial cluster centers by employing hierarchical method [11]. Another work uses the spectral algorithm to cluster the color histogram of frame images [10]. In artificial intelligence research community, automatic cluster based methods have a bright application prospect [9].…”

Section: Related Workmentioning

confidence: 99%

“…Cluster based techniques work by clustering together the similar frames and extracting one representative frame of each class. They avoid shot segmentation error of shot based methods, decrease inter-frame difference analysis frequency of content based methods, and can be well-suited to key frame extraction in related fields [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Extraction and Clustering for Static Video Summarization

Sun

Liu

Jiang

2021

Preprint

View full text Add to dashboard Cite

Numerous limitations of shot based and content based key frame extraction approaches have encouraged the development of cluster based methods. This work provides OTMW, Optimal Threshold and Maximum Weight clustering method, as a novel cluster based key frame extraction method. The video feature dataset is constructed by computing the color, texture and information complexity features of frame images. An optimization function is developed to compute the optimal clustering threshold. It is constrained by fidelity and ratio measure parameters. We turn to an empirical study on the proposed method in multi-type video key frame extraction tasks and compare it with popular cluster based methods including Mean-shift, DBSCAN, GMM and K-means. OTWM method achieves an average fidelity and ratio of 96.12 and 97.13, respectively. Experimental results demonstrate that OTMW can bring higher fidelity and ratio performance, while still maintaining a competitive performance over other cluster based methods. Overall, the proposed method can accurately extract key frames from multi-type videos.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Feature Extraction and Clustering for Static Video Summarization

Sun

Liu

Jiang

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Video summarization is the condensation of a video into a storyboard that contains key frames or several video segments, the goal of which is to make the video as small as possible, while keeping the flow of the story and essential parts of frames and/or video clips [11]. Video summarization provides a summarized version of video by analyzing the content of video.…”

Section: Defination Of Video Summarizationmentioning

confidence: 99%

New Approach in Video Summarization Based on Color Feature

Sarmadi¹

2017

View full text Add to dashboard Cite

Video summarization is a process to create a meaningful summary of the recorded video within a short period of time. In recent years due to increase in generating videos, we need to summarize videos. General video summarization methods are divided into static and dynamic. Static summary is done by using key frames. However, the dynamic video summarization includes a sequence of small video shots that have been put together in chronological order. In this paper, video summarization is done by using static method and color-based key frame extraction. Then by applying the super resolution algorithm on selected key frames, we obtained frames with higher visual quality. The summarization was performed on several videos. Simulation results show our method produces summaries with high visual quality and computational speed.

show abstract

Feature fusion and clustering for key frame extraction

Sun¹,

Jiang²,

Hu³

2021

MBE

View full text Add to dashboard Cite

<abstract> <p>Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarization. Firstly, the video content is analyzed using the image color, texture and information complexity, and video feature dataset is constructed. Then a Golden Section method is proposed to determine the threshold function optimal solution. The initial cluster center and the cluster number <italic>k</italic> are automatically obtained by employing the improved clustering algorithm. k-clusters video frames are produced with the help of K-MEANS algorithm. The representative frame of each cluster is extracted using the Maximum Weight method and an accurate video summarization is obtained. The proposed approach is tested on 16 multi-type videos, and the obtained key-frame quality evaluation index, and the average of Fidelity and Ratio are 96.11925 and 97.128, respectively. Fortunately, the key-frames extracted by the proposed approach are consistent with artificial visual judgement. The performance of the proposed approach is compared with several state-of-the-art cluster-based algorithms, and the Fidelity are increased by 12.49721, 10.86455, 10.62984 and 10.4984375, respectively. In addition, the Ratio is increased by 1.958 on average with small fluctuations. The obtained experimental results demonstrate the advantage of the proposed solution over several related baselines on sixteen diverse datasets and validated that proposed approach can accurately extract video summarization from multi-type videos.</p> </abstract>

show abstract

Consumer video summarization based on image quality and representativeness measure

Cited by 3 publications

References 21 publications

Feature Extraction and Clustering for Static Video Summarization

Feature Extraction and Clustering for Static Video Summarization

New Approach in Video Summarization Based on Color Feature

Feature fusion and clustering for key frame extraction

Contact Info

Product

Resources

About