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Efficient saliency analysis based on wavelet transform and entropy theory

机译：基于小波变换和熵理论的有效显着性分析

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摘要

Saliency detection has extensive applications in daily life. In this paper, an efficient saliency-detection method based on wavelet transform and entropy theory is proposed. In the algorithm proposed in this paper, salient regions are viewed as uncommon regions in the background of an image. The uncommon regions can be caused by differences in color, orientation, texture, shape, or other factors. Considering the fact that wavelet coefficients can represent the local features of an image in different scales and orientations, the wavelet transform is therefore employed to identify the salient regions. Unlike those conventional wavelet-based methods, our proposed method need not perform the inverse wavelet transformation; this can reduce the computational requirements. In addition, because the different factors (i.e. color, orientation, texture, shape, etc.) stimulate different aspects of the human visual system, a saliency-map combination scheme based on the entropy theory is devised in this paper, which can evaluate the influence or significance of the different factors. Experimental results show that our method, based on wavelet transformation and entropy theory, can achieve excellent performance in terms of the area under the receiver operating characteristic curve (AUC) score, the linear correlation coefficient (CC), the normalized scan-path saliency (NSS) score, and visual performance, as compared to existing state-of-the-art methods. (C) 2015 Elsevier Inc. All rights reserved.
机译：显着性检测在日常生活中具有广泛的应用。提出了一种基于小波变换和熵理论的有效显着性检测方法。在本文提出的算法中，显着区域被视为图像背景中的不常见区域。罕见区域可能是由颜色，方向，纹理，形状或其他因素的差异引起的。考虑到小波系数可以表示图像在不同比例和方向上的局部特征这一事实，因此采用小波变换来识别显着区域。与那些传统的基于小波的方法不同，我们提出的方法无需执行逆小波变换；这样可以减少计算需求。此外，由于不同的因素（即颜色，方向，纹理，形状等）刺激了人类视觉系统的不同方面，因此本文设计了一种基于熵理论的显着性图组合方案，该方案可以评估不同因素的影响或意义。实验结果表明，基于小波变换和熵理论的我们的方法在接收器工作特征曲线（AUC）得分，线性相关系数（CC），归一化扫描路径显着性（与现有的最新方法相比）。 （C）2015 Elsevier Inc.保留所有权利。

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