Howtosignalprocessing
- How are saliency maps generated?
- What does saliency map tell?
- What are saliency models?
- How could saliency map help to improve model performance?
How are saliency maps generated?
To create a saliency map of an image, first, we extract the image's basic properties such as color, orientation, and intensity. Then, these processed photos are used to create Gaussian pyramids to produce a features map. Finally, the saliency map is constructed by taking the average of all the feature maps.
What does saliency map tell?
The purpose of the saliency map is to find the regions which are prominent or noticeable at every location in the visual field and to guide the selection of attended locations, based on the spatial distribution of saliency. It is used in various Visual Attention models.
What are saliency models?
Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed.
How could saliency map help to improve model performance?
The intuition behind is straightforward: saliency maps generated from the pre-trained model contain “knowledge” of recongizing objects from the background, and when we fuse these saliency information to the model, the model can quickly detect the most representative area of the object and thus can learn useful features ...
