How Can PCA Be Used in Image Analysis [closed]

1. How PCA is used in image processing?
2. Can PCA be used for image classification?
3. How PCA works in image feature extraction?
4. When should PCA not be used?

How PCA is used in image processing?

One of the use cases of PCA is that it can be used for image compression — a technique that minimizes the size in bytes of an image while keeping as much of the quality of the image as possible.

Can PCA be used for image classification?

PCA is an image classification technique typically used for face recognition. Principal components are the distinctive or peculiar features of an image. The approach described in this paper uses this PCA capability for enhancing the accuracy of cloud image analysis.

How PCA works in image feature extraction?

PCA is an important method for feature extraction and image representation. In PCA, matrix transformation of the image takes place into high dimension vectors and its covariance matrix is obtained consuming high-dimension vector space.

When should PCA not be used?

PCA should be used mainly for variables which are strongly correlated. If the relationship is weak between variables, PCA does not work well to reduce data. Refer to the correlation matrix to determine. In general, if most of the correlation coefficients are smaller than 0.3, PCA will not help.