A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency detection for content-aware image resizing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hypercomplex Fourier Transforms of Color Images
IEEE Transactions on Image Processing
Proto-Object Based Rate Control for JPEG2000: An Approach to Content-Based Scalability
IEEE Transactions on Image Processing
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Saliency detection is an useful technique for image semantic analysis such as auto image segmentation, image retargeting, advertising design and image compression. Inspired by two existing saliency detection algorithms, named spectral residual (SR) and phase spectrum of quaternion Fourier transform (PQFT), we propose a new bottom-up saliency detection method which is featured with the introduction of hypercomplex spectral contrast (HSC) in saliency detection. The proposed HSC algorithm introduces the HSV color image vector space in hypercomplex number, and is better comprehensive to consider amplitude spectral contrast into saliency model as well as phase spectral contrast. Meanwhile, we also incorporate the human vision nonuniform sampling into our model, which is a common phenomenon that directs visual attention to the logarithmic center of image in natural scenes. Experimental results on two public saliency detection datasets show that our approach performs better than four state-of-the art approaches remarkably.