...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Dynamic Whitening Saliency
【24h】

Dynamic Whitening Saliency

机译：动态美白显着性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevant information is carried by high-order statistical structures. Through whitening, we completely remove the second-order information (correlations and variances) of the data, gaining access to the relevant information. The proposed approach is an analytically tractable and computationally simple framework which we call Dynamic Adaptive Whitening Saliency (AWS-D). For model assessment, the provided saliency maps were used to predict the fixations of human observers over six public video datasets, and also to reproduce the human behavior under certain psychophysical experiments (dynamic pop-out). The results demonstrate that AWS-D beats state-of-the-art dynamic saliency models, and suggest that the model might contain the basis to understand the key mechanisms of visual saliency. Experimental evaluation was performed using an extension to video of the well-known methodology for static images, together with a bootstrap permutation test (random label hypothesis) which yields additional information about temporal evolution of the metrics statistical significance.
机译：普通动态场景涉及多个刚性和柔性对象，它们具有相对运动和共同运动，无论是否引起照相机。运动事件的复杂性及其强大的时空相关性使动态视觉显着性的估计成为一个巨大的计算挑战。在这项工作中，我们基于感知相关信息由高阶统计结构承载的假设，提出了显着性计算模型。通过变白，我们完全删除了数据的二阶信息（相关性和方差），从而可以访问相关信息。所提出的方法是一个分析上易于处理且计算简单的框架，我们称之为动态自适应白化显着性（AWS-D）。对于模型评估，提供的显着性图用于预测六个公共视频数据集上人类观察者的注视，并在某些心理物理实验（动态弹出）下重现人类行为。结果表明，AWS-D击败了最新的动态显着性模型，并表明该模型可能包含理解视觉显着性关键机制的基础。使用众所周知的静态图像方法的视频扩展以及自举置换测试（随机标签假设）进行实验评估，该测试产生有关度量统计意义的时间演变的其他信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号