Download - Video Summarization Ppt
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Video summarization as the name implies, is a short
summary of the content of a longer video
The purpose of video summarization is to extract froma video, a limited number of key frames that convey themeaning of the whole video at a glance.
This project proposes to formulate videosummarization as a search problem and to use Genetic
Algorithms as the search algorithm.
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Genetic algorithms belong to the class of
evolutionary algorithms (EA), which generate
solutions for problems using techniques
inspired by natural evolution, such asinheritance, mutation, selection, and crossover.
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An original video consists of the following
components- Scenes, Shots and frames, in a
hierarchical order
The original video is split up into scenes and then to
shots which finally give frames.
In the video summarization process, only the frames
are considered. A group of selected frames are
combined together to form candidate summaries.
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Selection/Crossover/Mutation
Candidate Summaries
User
Summarized Video
Original Video
Scenes
Shots
Frames
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This forms the first step of thevideo summarization process
where the whole video is split upinto frames
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Defining the population size (CandidateSummaries)
User analysis of the generated summaries by givinga score to each of them
Generating the next set of summaries through theGA procedures of crossover and mutation, basedon the user given scores
Iterating the process until a preferable summary isobtained
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A text field is associated with each of the candidate summarieswhere the user can rate the summary by giving it a score of hischoice.
It is assumed that the one with the highest score forms the most
optimal summary.
This experimentation was carried out with four candidatesummaries. Four corresponding scores are given for each of the fourcandidate summaries.
The algorithm proceeds in such a way that top three scores aretaken and the summary with the highest score is crossed over andmutated with the other two to form a new set of candidate
summaries.
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The GA proceeds through the following stages
Selection Crossover Mutation
The population size is fixed as 4
Based on the user scores for each of thesummaries, the highest 3 scores are selectedand the last one is ignored
The crossover and mutation operations areperformed based on these 3 scores to generatethe next set of summaries
The mutation and crossover probabilities arerandomly generated and a threshold is set,above which mutation and crossover happens
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Once the scores are generated, the summaries are
processed by the genetic algorithm based on the
following two factors:
Crossover
Mutation
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Crossover is a genetic operator
It is analogous to reproduction and biological crossover,upon which genetic algorithms are based.
The first and the second summaries are taken and they are
combined together so that the frames of both the summariesare arranged in the temporal order.
Two new summaries are generated from this combinationsuch that the new summaries will have the alternate values
of the combination, i.e. the first value of the combinationgoes to the first summary, and second value goes to thesecond summary and so on.
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Mutation is a genetic operator used to maintain geneticdiversity from one generation of a population of algorithm
chromosomes to the next. It is analogous to biologicalmutation.
The purpose of mutation in GAs is to prevent the
population of chromosomes from becoming too similar toeach other, thus slowing or even stopping evolution. Thisexperiment implements mutation after the crossover.
After the generation of a new set of summaries after
crossover, the elements in it are mutated, i.e. values withinare replaced by a new set of values and are arranged in thetemporal order to preserve the semantics.
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Video surveillance: In surveillance video, instead of
viewing the whole video summary of video can be of
use.
Internet: In internet, before downloading a full video,
download the summary video and check if the video is
relevant or not in that case download the video
In movie trailers
In cricket highlights etc
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