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Unsupervized Video Summarization using RL.

train

Reward= Reward_Diversity + Reward_Representativeness.


Reward_Representativeness = Pixel Difference between two consecutive images ( MSE between them)

Changing it to

Reward_Representativeness = SSIM between two consecutive images

Where

SSIM : structural similarity (SSIM) index

luminance (light emitted) , contrast( difference b/w luminance) , image degradation()

Results (Initial) :

Number Video NewMetric Orignal_Metric
1 video_14 28.4% 28.4%
2 video_19 60.8% 28.6%
3 video_23 58.3% 61.7%
4 video_25 55.3% 55.3%
5 video_7 29.2% 29.1%
Number Video NewMetric Orignal_Metric
1 video_13 29.2% 50.2%
2 video_16 34.9% 34.9%
3 video_24 29.5% 29.5%
4 video_3 35.2% 35.2%
5 video_4 54.4% 47.7%

The Algorithm uses preprocessed data as its input .

Right now , working on getting from raw data to output + into matrix that what causes these changes.

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