Visual saliency estimation for images and video
Methods and apparatus for visual saliency estimation for images and video are described. In an embodiment, a process includes decomposing, by a processor, an image into elements, wherein each element includes at least one pixel. The processor then calculates a first image measure indicative of each element's uniqueness in the image on a per element basis, and a second image measure indicative of each element's spatial distribution in the image on a per element basis. A per element saliency measure is provided by combining the first image measure and the second image measure, or by utilizing the first image measure, or by utilizing the second image measure.
1. A method, comprising:
decomposing, by a processor, an image into elements, wherein each element comprises at least one pixel;
calculating, by the processor using a first Gaussian filter, a first image measure in linear time, the first image measure indicative of each element's uniqueness in the image on a per element basis;
calculating, by the processor using a second Gaussian filter, a second image measure in linear time, the second image measure indicative of each element's spatial distribution in the image on a per element basis; and
providing, by the processor, a per element saliency measure by combining the first image measure and the second image measure.
2. The method of claim 1 , further comprising generating, by the processor based on the per element saliency measure, a pixel-accurate image saliency map.
3. The method of claim 2 , wherein generating the pixel accurate saliency map comprises utilizing an up-sampling process that is both local and feature sensitive.
4. The method of claim 1 , wherein at least one of the first image measure and the second image measure is calculated based on at least one discriminating feature extracted from the pixels associated with each element.
5. The method of claim 4 , wherein the discriminating feature comprises at least one of a color feature, a texture feature, a disparity feature, or a motion feature.
6. The method of claim 1 , wherein decomposing comprises utilizing, by the processor, a modified simple linear iterative clustering (SLIC) superpixels protocol that utilizes K-means clustering in geodesic image distance in CEILab space.
7. The method of claim 1 , wherein combining the first image measure and the second image measure comprises:
normalizing uniqueness measures and distribution measures to a range; and
combining each normalized uniqueness measure with a corresponding distribution measure to obtain the per element saliency measure.
8. A non-transitory computer-readable medium storing instructions configured to cause a processor to:
decompose an image into elements, wherein each element comprises at least one pixel;
calculate using a first Gaussian filter, a first image measure in linear time, the first image measure indicative of each element's uniqueness in the image on a per element basis;
calculate, by using a second Gaussian filter, a second image measure in linear time, the second image measure indicative of each element's spatial distribution in the image on a per element basis; and
provide a per element saliency measure by combining the first image measure and the second image measure.
9. The computer-readable medium of claim 8 , further comprising instructions configured to cause the processor to generate, based on the per element saliency measure, a pixel-accurate image saliency map.
10. The computer-readable medium of claim 9 , wherein the instructions for generating the pixel accurate saliency map further comprise instructions configured to cause the processor to utilize an up-sampling process that is both local and feature sensitive.
11. The computer-readable medium of claim 8 , wherein the instructions for calculating the first image measure or the second image measure further comprise instructions configured to cause the processor to calculate the first image measure or the second image measure based on at least one discriminating feature extracted from the pixels associated with each element.
12. The computer-readable medium of claim 8 , wherein the instructions for decomposing further comprise instructions configured to cause the processor to utilize a modified simple linear iterative clustering (SLIC) superpixels protocol that utilizes K-means clustering in geodesic image distance in CEILab space.
13. The computer-readable medium of claim 8 , wherein the instructions for combining the first and second image measures further comprise instructions configured to cause the processor to:
normalize uniqueness measures and distribution measures to a range; and
combine each normalized uniqueness measure with a corresponding distribution measure to obtain the per element saliency measure.
14. An apparatus, comprising:
a processor;
a communication device operably connected to the processor; and
a storage device operably connected to the storage device, the storage device storing non-transitory instructions configured to cause the processor to:
decompose an image into elements, wherein each element comprises at least one pixel;
calculate using a first Gaussian filter, a first image measure in linear time, the first image measure indicative of each element's uniqueness in the image on a per element basis;
calculate, by using a second Gaussian filter, a second image measure indicative of each element's spatial distribution in the image on a per element basis; and
provide a per element saliency measure by combining the first image measure and the second image measure.