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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling
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Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling

机译：使用稀疏随机颜色样本和联合上采样进行快速显着性检测

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The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime.
机译：人类视觉系统采用视觉注意力机制，该机制仅选择部分传入信息以进行进一步处理。通过这种机制，大脑避免了其有限的认知能力超负荷。在计算机视觉中，通常通过显着性检测来完成此任务，显着性检测将输出图像周围环境所不同的区域。在许多技术应用中，例如图像压缩，视频质量评估和基于内容的图像检索，此功能是理想的。提出了一种基于颜色距离稀疏的随机样本和联合上采样的显着性检测方法。该方法通过利用Fast Global Smoother的边缘保留平滑和联合上采样功能，以较短的运行时间来计算全分辨率显着图。通过精确召回曲线，F测度和MSRA1K数据集的平均运行时间来评估所提出的方法。结果表明，该方法在显着性检测准确性和运行时间方面均与最新算法竞争。

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