User-centred personalised video abstraction approach adopting SIFT features

Darabi, Kaveh; Ghinea, Gheorghiţă

doi:10.1007/s11042-015-3210-4

Cited by 7 publications

(1 citation statement)

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“…These scores are fused into a global visual attention model where VS is achieved by selecting consecutive score peaks. In [23] a personalized saliency-based VS method was proposed. In that method, a score was assigned to each video frame based on their audio, visual and textual characteristics.…”

Section: Saliency-based Video Summarizationmentioning

confidence: 99%

SalSum: Saliency-based Video Summarization using Generative Adversarial Networks

Pantazis,

Dimas,

Iakovidis

2020

Preprint

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The huge amount of video data produced daily by camera-based systems, such as surveilance, medical and telecommunication systems, emerges the need for effective video summarization (VS) methods. These methods should be capable of creating an overview of the video content. In this paper, we propose a novel VS method based on a Generative Adversarial Network (GAN) model pre-trained with human eye fixations. The main contribution of the proposed method is that it can provide perceptually compatible video summaries by combining both perceived color and spatiotemporal visual attention cues in a unsupervised scheme. Several fusion approaches are considered for robustness under uncertainty, and personalization. The proposed method is evaluated in comparison to state-of-the-art VS approaches on the benchmark dataset VSUMM. The experimental results conclude that SalSum outperforms the state-of-the-art approaches by providing the highest f-measure score on the VSUMM benchmark.

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Section: Saliency-based Video Summarizationmentioning

confidence: 99%