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5 Common problems with working on real-world image data

31 March 2022

Processing can help you improve the quality of your image or extract essential information from it. It has applications in industries like medical imaging and may even be used to conceal data inside a picture.

 Real-world Image Processing applications.

  • Imaging in medicine
  • Security
  • Defense and military
  • Object detection
  • Image sharpening and reconstruction 

The technique of assigning labels to images based on their characteristics is known as image categorization.

One of the biggest challenges in image classification is getting a machine to learn the difference between a ‘dog’ and a ‘cat.’ We’ve managed to make some huge strides in the field of machine learning, but still there are certain things that a human can do better than a machine. Image classification is one of those tasks. We’ve compiled a list of some of the most common challenges that you could run into when trying to classify images

The main challenges in image classification are the large number of images, the high dimensionality of the data, and the lack of labeled data. Images can be very large, containing a large number of pixels. The data in each image may be high-dimensional, with many different features. There is also a shortage of labeled data, which is data that has been annotated with the correct class or category.

Consider the following types of images: puppies and kittens.

As a result, when we send an image of a specific category to an image classification system, the system assigns a label to the picture based on the category.

5 common Image categorization problems:

The following are the most significant issues in picture classification:

Intra-class variation 

Intra-class variance is the difference in images from the same class. Having flowers of various types in our dataset is an example of intra-class variance. They could be “Rose,” “Lilly,” or “Jasmine,” for example. The problem of intra-class variation can be solved using this picture categorization method.

Variation in Scale

This is a typical issue in image classification. Scale variance refers to having many images of the same subject at different sizes. If we have a photo of a scale variation of the same object let’s say chair, but they are all various sizes,

Variation in perspective

We have perspective variation, which allows an item to be oriented/rotated in several dimensions depending on how it is shot. The object still remains the same regardless of the angle from which we photograph it.


Our picture categorization system should be able to cope with variations in illumination as well. Let’s imagine we have two pictures of the same painting, each with a different amount of pixel intensity. Our picture categorization system should be able to adapt to lighting changes. So, if we offer our image classification system a picture of the same item with varying brightness levels (Illumination), the system should be able to assign them the same label.

Clutter in the Background

It signifies that the image contains a large number of things, making it difficult for the observer to locate the desired object. These photographs have a lot of “noise” to them.

However, we are only interested in one specific object in the photograph, which is difficult to distinguish owing to the “noise.” It’s a challenging assignment for a human, so think how difficult it is for a machine that has no conceptual knowledge of the image.

ConclusionImage classification is one of the most important elements in the field of machine learning. The main challenges associated with image classification are mainly due to the fact that images are just huge matrixes with a very large number of pixels present and they are very complex in nature. Training a machine to classify images correctly is a very tedious and complex task. In conclusion, image classification is a complex process that faces many challenges. These challenges include accurate detection and recognition of objects in images, as well as dealing with variations in lighting, scale, and orientation. Addressing these challenges is essential for developing reliable image classification systems. Follow us on https://www.linkedin.com/company/bayshoreintel/mycompany/ to get more insights on interesting topics.

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