What method would be appropriate for differentiating between polar bears and brown bears in images?

Image classification is an appropriate method for differentiating between polar bears and brown bears in images because it involves categorizing an entire image into a specific class or label based on the main subject present in the image. In this scenario, the goal is to identify whether the image contains a polar bear or a brown bear, which aligns perfectly with the capabilities of image classification. The system would analyze the visual features of the bears and assign a label indicating the species.

In contrast, object detection might be more suitable if the images contain multiple objects, as it identifies and locates instances of objects within an image. While it could technically be used to differentiate between the two species, it’s not the most straightforward method when classifying a single object in the image.

Optical character recognition (OCR) is designed for reading and digitizing text from images rather than identifying or categorizing animals. This technique is irrelevant in the context of distinguishing between types of bears.

Semantic segmentation provides a more detailed analysis by classifying each pixel in an image, which segments the image into regions corresponding to different objects or classes. While it could supply detailed information about the bears by marking them out, it represents a more complex approach than necessary for simply classifying the image into one of two categories