Inductive bias can be thought of as inductive preferences.
In machine learning, inductive preferences refer to some a priori assumptions (based on some experience) or restrictions adopted when training an algorithm to help the model better generalize and learn the characteristics of the data. (My understanding of it is that based on some previous experience, for example, CNN handles it better, then use this method)
In the field of image processing, a model can have some specific inductive preferences that guide its processing and understanding of image data. For example, Convolutional Neural Networks (CNN) is a widely used image processing model, and its structure itself is an inductive preference based on the locality and translation invariance of the image. By connecting convolutional layers and pooling layers in series, the CNN model can extract features with local structure and spatial hierarchy from original pixels, thereby better completing tasks such as image classification and target detection.
We can take advantage of image-specific inductive biases that our model offers
in order to improve the performance and effectiveness of image processing tasks. By taking full advantage of the model's inductive preferences, you can better extract meaningful features from images and perform efficient image analysis.