Rethinking the evaluation of video summaries
| Autor: | Mayu Otani, Esa Rahtu, Janne Heikkilä, Yuta Nakashima |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Protocol (science)
FOS: Computer and information sciences Measure (data warehouse) Information retrieval Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Automatic summarization Pipeline (software) Vision Applications and Systems Visualization Set (abstract data type) Datasets and Evaluation 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Segmentation Artificial intelligence business |
| Zdroj: | CVPR |
| Popis: | Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper, we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover, it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations, we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations. Comment: CVPR'19 poster |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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