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An Efficient Saliency Detection Model Based on Wavelet Generalized Lifting

机译：基于小波广义提升的有效显着性检测模型

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摘要

Saliency detection refers to the segmentation of all visually conspicuous objects from various backgrounds. The purpose is to produce an object-mask that overlaps the salient regions annotated by human vision. In this paper, we propose an efficient bottom-up saliency detection model based on wavelet generalized lifting. It requires no kernels with implicit assumptions and prior knowledge. Multiscale wavelet analysis is performed on broadly tuned color feature channels to include a wide range of spatial-frequency information. A nonlinear wavelet filter bank is designed to emphasize the wavelet coefficients, and then a saliency map is obtained through linear combination of the enhanced wavelet coefficients. This full-resolution saliency map uniformly highlights multiple salient objects of different sizes and shapes. An object-mask is constructed by the adaptive thresholding scheme on the saliency maps. Experimental results show that the proposed model outperforms the existing state-of-the-art competitors on two benchmark datasets.
机译：显着性检测是指对来自各种背景的所有视觉上明显的对象进行分割。目的是生产与人类视觉注释的显着区域重叠的对象蒙版。本文提出了一种基于小波广义提升的有效的自下而上的显着性检测模型。它不需要具有隐含假设和先验知识的内核。在广泛调谐的色彩特征通道上执行多尺度小波分析，以包含广泛的空间频率信息。设计非线性小波滤波器组以增强小波系数，然后通过增强小波系数的线性组合得到显着图。此全分辨率显着性图统一突出显示了不同大小和形状的多个显着对象。通过在显着图上采用自适应阈值方案构造对象蒙版。实验结果表明，在两个基准数据集上，所提出的模型优于现有的最新竞争对手。

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