Query-based video summarization with multi-label classification network

Hu, Weifeng; Li, Yujun; Zhao, Jia; Hu, Xifeng; Chen, Yan; Wang, Xuejing

doi:10.1007/s11042-023-15126-1

Cited by 7 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, the video background remains static while the methodologies employed revolve around the detection of individuals, objects, and events (13) . One of emerging research subfield is the query focused video summarization (14) which is a type of personalized video summarization. The output summary is highly dependent on the user query (15) .…”

Section: Introductionmentioning

confidence: 99%

A Framework for Video Summarization using Visual Attention Technique

Dhanushree,

Priya,

Aruna

et al. 2024

IJST

View full text Add to dashboard Cite

Objectives: To develop an efficient Video Summarization technique that aims to utilize the saliency map for mimicking the human way of selecting the important events in the given video. Methods: This paper proposes Histogram based Weighted Fusion (HWF) algorithm that uses spatial and temporal saliency maps to act as guidance in creating the summary of the video. The spatial saliency score and temporal saliency score obtained from the corresponding saliency maps are fused using the proposed HWF algorithm to obtain the frame level importance score. It tries to depict the visual attention of the human brain when watching a particular video. Findings: The experimental results show that the proposed HWF algorithm performs better than the state-of-the-art methods. Novelty: The use of Histogram intersection and the incorporation of the exponential function as the weight for the combined feature enhance the summarization ability of the proposed model. Keywords: Video Summarization, Saliency Map, Histogram intersection, Contrast sensitivity function, Attention curves

show abstract

Section: Introductionmentioning

confidence: 99%