Description: Patch extraction is the process of selecting small regions of an image for analysis. This approach is fundamental in the context of convolutional neural networks (CNNs), where the goal is to identify specific features within a larger image. By dividing an image into patches, the model can focus on local details, which can enhance accuracy in tasks such as image classification, object detection, and semantic segmentation. Each patch is processed individually, facilitating the identification of relevant patterns and features that may not be evident in the full image. Additionally, patch extraction can help reduce computational complexity, as it allows for working with more manageable input sizes. This method is also useful for increasing dataset diversity, as multiple patches can be generated from a single image, contributing to better model training. In summary, patch extraction is a key technique in deep learning that optimizes image analysis by focusing on specific regions, thereby improving the ability of neural networks to learn and generalize from visual data.
