Computation of intrinsic perceptual saliency in visual environments, and applications
Detection of image salience in a visual display of an image. The image is analyzed at multiple spatial scales and over multiple feature channels to determine the likely salience of different portions of the image. One application for the system is in an advertising context. The detection may be improved by second order statistics, e.g. mean and the standard deviations of different image portions relative to other portions. Different edges may be considered as being extended edges by looking at the edges over multiple spatial scales. One set of feature channels can be optimized for use in moving images, and can detect motion or flicker. The images can be obtained over multiple spectral ranges the user can be instructed about how to maximize the saliency. This can be applied to automatically evaluate and optimize sales or advertisement displays.
1. A method, comprising:
using a computer or processor to transform an image representation into a two dimensional spatial map to predict which parts in said image may be perceived as visually conspicuous by a human observer, the transforming comprising:
using the computer or processor to obtain information about the image in at least a first spectral range and a second spectral range that is a different spectral range than said first spectral range; and
using the computer or processor to correlate said information about the image in said first spectral range to information in said second spectral range, determine salient portions of the image from said correlating, without looking for specific content of the image.
2. A method as in claim 1 , wherein said analyzing comprises using said salient portions to determine an effectiveness of said image when displayed in a display medium.
3. A method as in claim 2 , wherein said analyzing comprises analyzing pixels of said image using mean pixel values.
4. A method as in claim 2 , wherein said analyzing comprises analyzing pixels on said image using second higher order statistical variations.
5. A method as in claim 2 , wherein said image comprises a single image at a single time.
6. A method as in claim 2 , wherein said image comprises a sequence of images in time representing a moving scene.
7. A method as in claim 1 , further comprising using said analyzing to optimize a display of visual information.
8. A method as in claim 1 , wherein the image is received from an image sensor.
9. A method as in claim 8 , wherein the image sensor is a multi-spectral image sensor.
10. A method as in claim 9 , wherein the multi-spectral image sensor operates in at least one of an infra-red spectrum or a visible spectrum.
11. A method as in claim 2 , wherein the display medium is at least one of an electronic display medium or a print medium.