Dive deep into data augmentation with image processing & know what the future looks like in our latest blog.
Image classification is a process of comprehending an entire image by a machine using machine learning just as a human would. While understanding the contents of an image, the main idea is to categorize it by assigning it one or more labels. It involves extracting the image's main characteristics to classify it in a predefined set of classes or tags. Since classifying an image or an object in an image is challenging, manually examining and classifying images may be time-consuming, especially with larger datasets. Therefore, automating the entire process using computer vision can lead to huge benefits for organizations. Thus, image classification is a crucial task in computer vision. Data Scientists can leverage the power of image classification to solve challenging real-world problems in image processing and recognition.
Image classification has brought technical breakthroughs in various areas, including the automobile industry, healthcare, manufacturing, and others. Image annotation is a tedious task if done manually. Automating this can help companies build better image classification models faster for deployment. Currently, we can see image classification revolutionizing the areas such as -
With advancements in image classification, we can improve the image recognition capabilities of machines and leverage them in the above fields.
Although with the significant progress in machine learning in the recent past, there are still some things humans can do better than machines. One such task is image categorization or assigning correct labels to an image based on its contents. For example, it is difficult for a computer to identify the correct cuisine of different food items as the dish presentation looks different due to a garnishing change.
It is evident that the size and complexity of data and the availability of labeled data are the key obstacles in image classification. Other than these, the following are a few challenges that are likely to be experienced in image classification -
The next challenge in image classification is that of perspective variation, where a different orientation of an object poses difficulty for the trained image classification model to categorize correctly. When an object such as a book is shot at different angles, the images look different to a machine even though the object remains the same.
Figure 1: View-Point Variation
This type of variation is experienced when the images from the same class appear different. A typical example is 'vehicles' where a four-wheeler like an "SUV" or a "Hatchback" would appear different to a computer. Another example could be for the category of 'flowers' where "Rose", "Orchid", “Sunflower” or "Daisy" would look different. To address this challenge, we can use the picture categorization method, i.e., creating an optimum number of subclasses to train the model.
Figure 2: Intra-Class Variation
Another typical issue faced in image classification is 'Scale Variation', where the machine fails to correctly categorize the image due to many images of the same object at different scales. An example of this could be simple objects like chairs or bottles of various shapes and sizes.
Figure 3: Scale Variation
Occlusion happens when two or more things are placed extremely close together and appear to overlap. In such cases, the image processing task becomes challenging, and the image classification model often tags the occluded objects incorrectly. The occlusion situation is encountered in the real-world where pixels of the object to be identified could appear to mix or join something closer to the viewer. A simple example could be pictures of a dog taken in different environments as shown -
Figure 4: Occlusion Variation
The image classification model faces a challenge when dealing with different lighting or image filters. The same image with different pixel intensity would appear different to the model.
Figure 5: Illumination or Color Variation
When an image for classification has multiple entities, the model cannot correctly distinguish the objects and classify them. Such images tend to feature undesirable noise. Sometimes this task can be difficult for humans as well. Consider the sample images given below. A white-colored puppy in snow, a squirrel on trees in a jungle, and cycles parked in the rain could be complicated for the model to classify as these tend to feature a lot of background clutter.
Figure 6: Clutter in the Background
Most of these challenges can be overcome by training the model on a more extensive and diverse dataset. To achieve this, a large dataset must be available with various images for the included labels. In reality, this is not always possible as acquiring new and a lot of data is time-consuming and tedious. Imagine getting such data in the medical field for diseases where the data available is extremely small or simply no previous dataset is available. At times, the data could also be unavailable due to privacy laws. Here enters Data Augmentation.
A deep learning model’s accuracy depends on the quality, amount, and relevance of training data. However, a lack of data is one of the most prevalent problems in applying machine learning. Gathering such data may be costly and time-consuming in many circumstances.
Data augmentation is a process for increasing the size of the existing dataset artificially by producing additional data points from current data.
Such an extended dataset can be created by making minor adjustments to data or utilizing deep learning models to produce additional data points. Companies can use data augmentation to reduce their requirement to collect training data and its preparation. This way, they can build more accurate machine learning models faster.
Several Python libraries are currently available for augmenting image datasets. Image DA (Data Augmentation) libraries like Augmentor, Albumentations, Imgaug, AugLy, Solt can be used to generate additional images from the existing images. Moreover, TensorFlow, Keras and PyTorch frameworks include image data augmentation capabilities. Any of these packages or frameworks can be used depending upon convenience and project requirements. Explore more about the tools and techniques here.
The existing dataset can be augmented using different techniques which manipulate the image parameters like size, color intensity, brightness and contrast, orientation, background, etc. The open- source Python libraries mentioned in the previous section allow manipulating images with these techniques. Let us explore a few of these techniques on a sample image of a car.
One of the techniques for augmentation is ‘Flipping’. We can flip the original image both horizontally and vertically to add two more views of the same object to the existing dataset. A vertically flipped image of a car might be helpful to classify an overturned vehicle in case of object detection.
Similarly, we can rotate the image at different angles to expose the model to more observations of the object. A vertically flipped image can also be achieved using the rotation technique with an angle of 180 degrees.
Another technique in Data Augmentation is ‘Scaling’. Using this technique, we can achieve different sizes of the same object for the model to generalize better for size changes.
With the Translation technique, we can shift the focus of the image to generate additional images of the same object as shown below -
Likewise, by adjusting the brightness and contrast or colors of the image, we can create more observations for the deep learning model.
Additionally, it is also possible to create new observations by zooming in/out or cropping the existing image.
Moreover, using an image augmentation library like Albumentations, we can add weather effects to an image such as rain, clouds or snow. Following augmented image is a transformed image with Albumentations for raining effect i.e. less brightness with blur effect and added drops on the image.
There are many more techniques available which can be explored and applied as per the dataset requirements. It is important to note that some techniques are better suited to a particular dataset being used and hence, choosing the correct set of techniques is essential for the success of Data Augmentation.
From the above techniques, it is quite clear that data augmentation allows expanding the existing dataset by adding more relevant observations quickly. Since the deep learning model requires a larger dataset for training, using data augmentation allows the model to generalize better with a variety of images belonging to different labels. Thus, it tends to increase the overall accuracy of the model in classification tasks. One thing to remember here is that data augmentation has a limitation in terms of over-fitting. Expanding the dataset with irrelevant and repetitive images might do more harm than help. So, it is crucial not to go overboard with image augmentation.
Data Augmentation has shown tangible benefits in the recent past in the areas of image reconstruction where old photos could be restored or their resolution could be improved using Deep learning. Additionally, it has been successful in the areas of medical imaging for disease identification and progress, environment monitoring for disaster management, control of autonomous vehicles and traffic management and so on. With advancements in computer vision, data augmentation will become a valuable tool for advanced data analytics for identification, classification and forecasting requirements.
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