A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural network implementation of a saliency map model
Neural Networks
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation
IEEE Transactions on Image Processing
Hypercomplex Fourier Transforms of Color Images
IEEE Transactions on Image Processing
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In this paper, we propose a saliency detection model based on amplitude spectrum. The proposed model first divides the input image into small patches, and then uses the amplitude spectrum of the Quaternion Fourier Transform (QFT) to represent the color, intensity and orientation distributions for each patch. The saliency for each patch is determined by two factors: the difference between amplitude spectrums of the patch and its neighbor patches and the Euclidian distance of the associated patches. The novel saliency measure for image patches by using amplitude spectrum of QFT proves promising, as the experiment results show that this saliency detection model performs better than the relevant existing models.