Image classification is the process that includes the categorization and labeling of different groups of vectors or pixels within one image based on specific requirements and rules. This process can be classified under 2 methods:

Supervised image classification: Process of image classification where classification stage is required and has been completed with previously labeled copies.

Unsupervised image classification: Process of classification where every image in a dataset is identified as a member of one inherent category present in image collection, without a need for previously labeled training copies.

Despite its simplicity, there are a few  common challenges related to this process:

1. Occlusion- When the object of interest is occluded, only a small part of the object is visible.
2. Deformation- Not all objects are rigid enough to endure the processing, and there are numerous ways of object deformation known.
3. Scale variation- Visual classes can contain a variation in their real-world size, and they aren’t valid for further processing.
4. Variations of the viewpoint- These variations arise from the fact that a single instance of the object can be oriented in multiple ways with regard to the camera.

To successfully complete the image classification process you can follow our formalized steps:

1. Each image you plan to incorporate into the process should be labeled with one of 2 different classes, and we use that as an input. Number images you choose after unbiasing the dataset in order to mark valid and valuable data for your desired outcome.
2. Use that data to learn a classifier the appearance and meaning of any classes included in the process.
3. Test your classifier by asking it to predict labels for a new set of images that hasn’t been incorporated ever before. After he finishes the task, you should compare the true labels of those images with the ones that the classifier predicted.