Hierarchical Keyframe-based Video Summarization Using QR-Decomposition and Modified -Means Clustering

Amiri, Ali

doi:10.1155/2010/892124

Cited by 25 publications

(11 citation statements)

References 48 publications

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“…The cluster-based key frames extraction methods are also very popular. In [ 4 ], a method based on sparse coding and k-means clustering is used to extract key frames, and added some conditions to select the number of key frames from each shot. However, these conditions are relatively harsh, and the constraint conditions of the sparse coding vector quantization method are too strict, which leads to more complicated parameters for extracting key frames.…”

Section: Related Workmentioning

confidence: 99%

“…The second major category of key frame extraction is based on clustering, where frames are clustered into groups according to the principle of similarity. In [ 4 ], a modified k-means clustering method is used to extract key frames. In [ 5 ], video summarization is formulated as a sequential decision, and treated diversity reward measure as a k-medoids problem; therefore, cluster centers are selected as key frames.…”

Section: Introductionmentioning

confidence: 99%

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Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Zhang

et al. 2020

Entropy

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This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and Fmeasure are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Zhang

et al. 2020

Entropy

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“…e former can divide data at one time to determine all classifications, while the latter need to recursively classify in a cohesive or split way. Amiri and Fathy [17] use an improved K-means algorithm to cluster shot-level key frames. Compared with the traditional K-means, the algorithm can obtain the number of clusters adaptively.…”

Section: Related Workmentioning

confidence: 99%

Key‐Frame Extraction Based on HSV Histogram and Adaptive Clustering

Zhao

Wang

et al. 2019

Mathematical Problems in Engineering

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Along with the fast development of digital information technology and the application of Internet, video data begins to grow explosively. Some applications with high real-time requirements, such as object detection, require strong online video storage and analysis capabilities. Key-frame extraction is an important technique in video analysis, which provides an organizational framework for dealing with video content and reduces the amount of data required in video indexing. To address the problem, this study proposes a key-frame extraction method based on HSV (hue, saturation, value) histogram and adaptive clustering. The HSV histogram is used as color features for each frame, which reduces the amount of data. Furthermore, by using the transformed one-dimensional eigenvector, the fixed number of features can be extracted for images with different sizes. Then, a cluster validation technique, the silhouette coefficient, is employed to get the appropriate number of clusters without setting any clustering parameters. Finally, several algorithms are compared in the experiments. The density peak clustering algorithm (DPCA) model is shown to be more effective than the other four models in precision and F-measure.

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“…Each block shows its step in the algorithm and the items produced there tion as keyframe alignment. Although there are also many methods that do not detect shot boundaries [5,12,13], dividing the whole video into subshots contributes to stabilize the keyframe extraction results [3,4,[14][15][16][17]. Given its importance, shot boundary detection is still a much studied topic [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

A static video summarization method based on the sparse coding of features and representativeness of frames

Jeong

Yoo

Cho

2016

J Image Video Proc.

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This paper presents a video summarization method that is specifically for the static summary of consumer videos. Considering that the consumer videos usually have unclear shot boundaries and many low-quality or meaningless frames, we propose a two-step approach where the first step skims a video and the second step performs content-aware clustering with keyframe selection. Specifically, the first step removes most of redundant frames that contain only little new information by employing the spectral clustering method with color histogram features. As a result, we obtain a condensed video that is shorter and has clearer temporal boundaries than the original. In the second step, we perform rough temporal segmentation and then apply refined clustering for each of the temporal segments, where each frame is represented by the sparse coding of SIFT features. The keyframe selection from each cluster is based on the measure of representativeness and visual quality of frames, where the representativeness is defined from the sparse coding and the visual quality is the combination of contrast, blur, and image skew measures. The problem of keyframe selection is to find the frames that have both representativeness and high quality, which is formulated as an optimization problem. Experiments on videos with various lengths show that the resulting summaries closely follow the important contents of videos.

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Hierarchical Keyframe-based Video Summarization Using QR-Decomposition and Modified -Means Clustering

Cited by 25 publications

References 48 publications

Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Video Summarization Based on Mutual Information and Entropy Sliding Window Method

Key‐Frame Extraction Based on HSV Histogram and Adaptive Clustering

A static video summarization method based on the sparse coding of features and representativeness of frames

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