Video summarization generation with self-attention and random forest regression

Ziyan Wang

doi:10.1117/12.2683282

26 June 2023 Video summarization generation with self-attention and random forest regression

Ziyan Wang

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127211F (2023) https://doi.org/10.1117/12.2683282
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Video summarization is used widely in the field of fast browsing and retrieval of videos by generating keyframes or segments to achieve video compression. Existing methods mostly explore based on image content, ignoring the temporal characteristics of videos, resulting in summaries lacking temporal coherence and representativeness. We propose a video summarization network based on an encoder-decoder framework. Specifically, the encoder part extracts features using a convolutional neural network and enhances the weight of key features through self-attention mechanism. The decoder part consists of a bidirectional long short-term memory network fused with a random forest, and adjusts the proportion of the random forest and bidirectional long short-term memory network in the loss function to make the model more stable and accurate in prediction. Experiments were conducted on two datasets and compared with seven other methods, and the comprehensive experimental results prove the effectiveness and feasibility of the proposed method.

Citation Download Citation

Ziyan Wang "Video summarization generation with self-attention and random forest regression", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127211F (26 June 2023); https://doi.org/10.1117/12.2683282

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KEYWORDS

Video

Random forests

Video coding

Data modeling

Image segmentation

Video processing

Video compression

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