Classical saliency-based visual attention models are adapted for embedding real-time systems with less time and space costs based on approximate Gaussian pyramids of the input image. Firstly, the circular window and discrete Gaussian convolution are approximated by rectangular window and rectangular average operator respectively. Then, rectangular average operator is implemented through “row accumulation followed by column accumulation”. And conspicuity maps of each channel are calculated and sampled at desired intervals directly with linear computational complexity on the number of the input pixels. At last, a fast algorithm for inhibiting the saliency of extracted regions in the saliency map is proposed. Experimental results in the images from Berkeley segmentation dataset validate that the proposed methods have much less computational costs with acceptable outputting errors. The two approximate methods in this paper can also be applied in other image processing problems in embedding real-time systems.