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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Scale-Invariant Amplitude Spectrum Modulation for Visual Saliency Detection
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Scale-Invariant Amplitude Spectrum Modulation for Visual Saliency Detection

机译：用于视觉显着性检测的标度不变幅度频谱调制

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摘要

Saliency detection is one of the key issues in simulating visual attention selection. Most attention models adopt the competitive structure to simulate the human visual system. Although these models provide remarkable results and convincing biological plausibility, they are still confronted with many difficulties in practical applications because of their extreme time cost and parameter sensitivity. Recently, a new saliency detection approach based on Fourier transform, as represented by spectral residual (SR) and phase Fourier transform (PFT), has been attracting much attention for its excellent accuracy and computational speed. All these models can be unified into one framework called amplitude spectrum modulation (ASM). The aim of this paper is to explore the intrinsic mechanism of ASM and develop an advanced ASM model. After analyzing SR and PFT, we give a mathematical description for the fundamental idea and the inherent limitations of the existing ASM models. A new saliency detective model, based on the scale-invariant ASM, scene and context-based modulation, and competitive structure, is also proposed breaking through the limitations of the traditional ASM models. Simulation results suggest that the proposed model is more accurate in predicting human eye fixation and is more robust against different types of stimulus when compared with competing models.
机译：显着性检测是模拟视觉注意选择的关键问题之一。大多数注意力模型采用竞争性结构来模拟人类视觉系统。尽管这些模型提供了卓越的结果并令人信服，但由于其极端的时间成本和参数敏感性，它们在实际应用中仍然面临许多困难。最近，以频谱残差（SR）和相位傅里叶变换（PFT）为代表的基于傅里叶变换的新显着性检测方法因其出色的准确性和计算速度而备受关注。所有这些模型都可以统一到一个称为幅度频谱调制（ASM）的框架中。本文的目的是探索ASM的内在机理，并开发一种先进的ASM模型。通过分析SR和PFT，我们对现有ASM模型的基本思想和固有局限性进行了数学描述。提出了一种基于尺度不变ASM，基于场景和上下文的调制以及竞争结构的新的显着性检测模型，该模型突破了传统ASM模型的局限性。仿真结果表明，与竞争模型相比，所提出的模型在预测人眼注视方面更准确，并且对不同类型的刺激更鲁棒。

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