基于多特征图像视觉显著性的视频摘要化生成

如何高效提取视频内容即视频摘要化，一直是计算机视觉领域研究的热点。简单通过图像颜色、纹理等特征进行检测已无法有效、完整地获取视频摘要。基于视觉注意力金字塔模型，提出了一种改进的可变比例及双对比度计算的中心-环绕视频摘要化方法。首先，以超像素方法对视频图像序列进行像素块划分以加速图像计算；然后，检测不同颜色背景下的图像对比度特征差异并进行融合；最后，结合光流运动信息，合并静态图像与动态图像显著性结果提取视频关键帧，在提取关键帧时，利用感知哈希函数进行相似性判断完成视频摘要化生成。在Segtrack V2、ViSal及OVP数据集上进行仿真实验，结果表明:所提方法可以有效提取图像感兴趣区域，得到以关键帧图像序列表示的视频摘要。 

Video summary generation based on multi-feature image and visual saliency

How to extract video content efficiently, that is, video summarization, is a research hotspot in the field of computer vision. Video summary cannot be obtained effectively and completely by simply detecting the image color, texture and other features. Based on the visual attention pyramid model, this paper proposes an improved center-surround video summarization method with variable ratio and double contrast calculation. First, the video image sequence is divided into pixel blocks by superpixel method to speed up image calculation. Then, the contrast feature difference under different color backgrounds is detected and fused. Finally, combined with the optical flow motion information, the static and dynamic saliency results are merged to extract the key frames of the video. When extracting the key frames, the perceived Hash function is used to perform similarity judgment to complete the video summary generation. Simulation experiments are carried out on Segtrack V2, ViSal and OVP datasets. The experimental results show that the proposed method can be used to effectively extract the area of interest, and finally obtain the video summary expressed by the sequence of key frame images.