Image classification and image segmentation are two different approaches in image recognition tasks. Here are the key differences between them:

  1. Definition and Output:

  - Image classification aims to classify an entire image into predefined categories or labels. The output of image classification is a single label that represents the entire image.

  - Image segmentation, on the other hand, involves dividing the image into multiple regions or segments based on common visual characteristics. The output of image segmentation is a pixel-level mask that indicates different regions or objects in the image.

  2. Spatial Information:

  - Image classification disregards the spatial arrangement of objects within an image. It treats the entire image as a single entity and focuses on identifying the dominant features.

  - Image segmentation takes into account the spatial layout of objects in an image. It assigns different labels to individual pixels or regions, considering their spatial relationships.

  3. Object-level vs Pixel-level:

  - Image classification works at the object level, considering the overall appearance and features of objects present in the image.

  - Image segmentation operates at the pixel level, dividing the image into smaller regions or segments based on the similarity of pixels within each segment.

  4. Granularity:

  - Image classification provides a high-level understanding of an image, focusing on differentiating between large categories such as animals, vehicles, or buildings.

  - Image segmentation provides a more detailed localization and identification of objects, enabling a finer-grained understanding of an image.

  5. Applications:

  - Image classification is commonly used in tasks like object recognition, scene understanding, and content-based image retrieval.

  - Image segmentation finds applications in tasks such as object detection, image editing, medical imaging, and autonomous driving.

  In summary, image classification categorizes entire images into predefined labels, while image segmentation divides images into smaller regions or pixels to identify and locate objects.