STIMO: STIll and MOving video storyboard for the web scenario

Furini, Marco; Geraci, Filippo; Montangero, Manuela; Pellegrini, Marco

doi:10.1007/s11042-009-0307-7

Cited by 229 publications

(111 citation statements)

References 22 publications

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“…The user summaries for each video in this dataset are also available. Based on this dataset, we compared our method with OV [3], DT [4], STIMO [5], and VSUMM [6] which is the clustering-based method using color features and kmeans algorithm. The difference between VSUMM 1 and VSUMM 2 in Table 1 is that one key frame is selected per either cluster or keycluster which is larger than the average cluster size.…”

Section: Resultsmentioning

confidence: 99%

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Song

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYKey frame based video summarization has emerged as an important task for efficient video data management. This paper proposes a novel technique for key frame extraction based on chaos theory and color information. By applying chaos theory, a large content change between frames becomes more chaos-like and results in a more complex fractal trajectory in phase space. By exploiting the fractality measured in the phase space between frames, it is possible to evaluate inter-frame content changes invariant to effects of fades and illumination change. In addition to this measure, the color histogram-based measure is also used to complement the chaos-based measure which is sensitive to changes of camera /object motion. By comparing the last key frame with the current frame based on the proposed frame difference measure combining these two complementary measures, the key frames are robustly selected even under presence of video fades, changes of illumination, and camera/object motion. The experimental results demonstrate its effectiveness with significant improvement over the conventional method.

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Section: Resultsmentioning

confidence: 99%

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Song

et al. 2016

IEICE Trans. Inf. & Syst.

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“…As already mentioned, the method was tested with the user-annotated database of the VSUMM method by de Avila et al [2,22], and compared to the results obtained with other methods such as OV [37], DT [38], STIMO [16], as well as VSCAN by Mahmoud et al [35]. Table 2 contains averaged F values of the obtained results of these methods and the tensor method proposed in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…In their method, keyframebased video summarization is computed with the Delaunay clustering [29]. In the STIMO system proposed by Furini, a method for moving video storyboard for the web scenario is proposed [16]. Their method is optimized for Web operation to produce on-the-fly video storyboards.…”

Section: Related Workmentioning

confidence: 99%

“…The next simple modification is initialization of the starting values of the eigenvectors with these values computed in the previous iteration, rather than initialization with random values, as presented in (16). This relies on a simple observation that the computed eigenvectors do not lie far from each other between consecutive iterations.…”

Section: Efficient Computation Of the Dominating Tensor Subspacementioning

confidence: 99%

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Tensor-Based Shot Boundary Detection in Video Streams

Cyganek

Woźniak

2017

New Gener. Comput.

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This paper presents a method for content change detection in multidimensional video signals. Video frames are represented as tensors of order consistent with signal dimensions. The method operates on unprocessed signals and no special feature extraction is assumed. The dynamic tensor analysis method is used to build a tensor model from the stream. Each new datum in the stream is then compared to the model with the proposed concept drift detector. If it fits, then a model is updated. Otherwise, a model is rebuilt, starting from that datum, and the signal shot is recorded. The proposed fast tensor decomposition algorithm allows efficient operation compared to the standard tensor decomposition method. Experimental results show many useful properties of the method, as well as its potential further extensions and applications.

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“…The goal of a video summarization is to provide a crisp video visualization so that the user can understand overall content of video [3] and remove redundant information from the video. Generally, video summarization include the steps like video segmentation, feature extraction, after that redundancy detection based on features and finally video summarization with the non redundant features (key frame) [4].…”

Section: Introductionmentioning

confidence: 99%

Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures

Patil¹,

Thepade²,

Sonare³

2015

IJCA

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In Video summarization is a method to reduce redundancy and generate succinct representation of the video data. In video summarization process, several frames containing similar information need to get processed, this leads to slower processing speed and higher complexity, consuming. More time Video summarization using key frames can ease the speed up of video processing. One of the mechanisms to generate video summaries is to extract key frames which represent the most important content of the video by identifying neard duplicate frames in video. In this paper, novel key frames extraction method is proposed with Thepade's Walsh Hademard Error Vector Rotation (THdEVR) with ten different codebook sizes and and assorted similarity measures. Experimentation done with help of the test bed of videos has shown that higher codebook sizes give better completeness in key frame extraction for video summarization. Experimental results are discussed for video content summarization with five assorted similarity measures like Euclidean Distance, Canberra Distance, Square-Chord Distance, Mean Square Error, Sorensen Distance with proposed THadVR.

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STIMO: STIll and MOving video storyboard for the web scenario

Cited by 229 publications

References 22 publications

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Tensor-Based Shot Boundary Detection in Video Streams

Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures

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STIMO: STIll and MOving video storyboard for the web scenario

Cited by 229 publications

References 22 publications

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Tensor-Based Shot Boundary Detection in Video Streams

Hadamard based Video Key Frame Extraction using Thepade&apos;s Transform Error Vector Rotation with Assorted Similarity Measures

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Product

Resources

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Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures