Video Partitioning and Secured Keyframe Extraction of MPEG Video

Thakre, Kalpana S.; Rajurkar, Archana M.; Manthalkar, Ramchandra

doi:10.1016/j.procs.2016.02.058

Cited by 31 publications

(8 citation statements)

References 12 publications

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“…The results were also compared with other existing techniques. The proposed technique achieves accuracy comparable to those provided by [ 3 , 10 , 30 , 38 , 54 ]. It can be tested for qualitative as well as quantitative features, but an actual test is only valid if the video summary is used in the applications for which it was created.…”

Section: Results and Analysismentioning

confidence: 59%

“…The frames matching a threshold are treated as keyframes [ 29 ]. Thakre et al proposed a technique for keyframe selection of compressed video shots using the adaptive threshold method working on 200 plus video clips [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

“…It has been found that, usually, 1–2 frames per second creates the illusion of movement. The rest of the frames show almost the same scene repeatedly [ 30 ]. Feature Extraction .…”

Section: The Proposed Median Of Entropy Of Mean Frames (Mme) Technmentioning

confidence: 99%

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Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Saqib

Kazmi

2018

Entropy

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Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results.

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Section: Results and Analysismentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

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Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Saqib

Kazmi

2018

Entropy

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“…The existing CBVR systems are based on low level features [10]. We can't describe the complete video content with the help of low level features since video description varies from person to person with respect to psycho visual system [20]. To design a CBVR system, first, what type of video database we are working on has to be decided?…”

Section: Cbvr System Designmentioning

confidence: 99%

A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features

2019

IJRTE

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There has been a revolution in multimedia with technological advancement. Hence, Video recording has increased in leaps and bounds. Video retrieval from a huge database is cumbersome by the existing text based search since a lot of human effort is involved and the retrieval efficiency is meager as well. In view of the present challenges, video retrieval based on video content prevails over the existing conventional methods. Content implies real video information such as video features. The performance of the Content Based Video Retrieval (CBVR) depends on Feature extraction and similar features matching. Since the selection of features in the existing algorithms is not effective, the retrieval processing time is more and the efficiency is less. Combined features of color and motion have been proposed for feature extraction and Spatio-Temporal Scale Invariant Feature Transform is used for Shot Boundary Detection. Since the characteristic of color feature is visual video content and that of motion feature is temporal content, these two features are significant in effective video retrieval. The performance of the CBVR system has been evaluated on the TRECVID dataset and the retrieved videos reveal the effectiveness of proposed algorithm.

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“…This results in several redundant representative frames [6] [10]. To get rid of these repetitive crucial frameworks, a filtering system action is carried out, where each representative frame is compared to every other representative frame to discover the duplicate or near-duplicate structures [14] [15]. The implementation of this algorithm is shown in the figure 3.…”

Section: Extraction Of Ultimate Representative Frames Using and Roughmentioning

confidence: 99%

An Effective Scheme for Shot Boundary Detection and Key Frame Extraction for Video Retrieval

Kumar*,

Reddy

2019

IJRTE

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The rapid development of devices for image capture and information sharing has resulted in the availability of huge amounts of online video for various applications such as education, news, entertainment, etc. This leads to problems and difficulties when users query any content-related video. The reason for this scenario is that the presently available techniques of content representation and retrieval are based primarily on annotation. It therefore provides insufficient information for understanding and retrieving the content to match the query of the user. Content Based Video Retrieval (CBVR) is one of the promising new ways for finding content in a large video archive, rather than simply searching terms. The primary steps for indexing, summarizing and retrieving video are shot transition recognition and representative frame extraction. We have proposed a key point matching algorithm for a superior and robust Scale Invariant Feature Transform (SIFT) followed by the collection of representative frames from each segmented shot using the Image Information Entropy method. By using the Rough Set Theory, we can get better the concert of this scheme through removing unnecessary representative frames. All the methods suggested to prove the efficacy were tested on TRECVID datasets and contrasted with state-of - the-art approaches

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Video Partitioning and Secured Keyframe Extraction of MPEG Video

Cited by 31 publications

References 12 publications

Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features

An Effective Scheme for Shot Boundary Detection and Key Frame Extraction for Video Retrieval

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