1. Evaluation unsupervised methods with different metric under same configuration.
2. Investigating effect of extracted features in unsupervised methods and extend it to perform better than baseline work.
3. Identify gaps form last step and try to fill by porposed solution by extending or modifiying an existing model.
Abstract:
This study validates the recent work in unsupervised video summarization andextends the experiments with feature variation.
In our understanding, deeplearning-based approaches select frame-candidates for a video summary.
Theseframes are drawn with a probability that can be used to calculate the scaled im-portance for comparison with the basic truth as an evaluation.
However, mostworks in the literature focus on network architecture and follow the same fea-ture extraction technique using the resulting deep features from one pretrainedmodel.
At this point a notable gap of the existing approaches is feature varia-tion, which this study explores.
Building on that, model variables like networkarchitecture, optimizer, and activation functions can have an impact on the per-formance combined with different feature selection techniques that haven’t beenexplored yet.Further, the Evaluation of existing models is conducted using one metric,which may be not representative in video summarization task, since it couldignore key-frames in favor of other frames and still have a relatively high value.This work performs feature extraction using multiple pretrained neural models,and then measures the impact of them on current state-of-the-art works.Then,it evaluates the state-of-the-art works using different evaluation metrics.
Even-tually, it aims to find an unsupervised video summarization method, in orderto fill the gaps, and leverage the existing works.
