By: Daksh Alpesh Shah, RIG Intern Research

Fig 1. Image Recognition

 

We as humans can easily differentiate between places, objects, and other living beings from images. This is because our brains have been trained with the same set of images that has resulted in the development of the capability to differentiate; the computers lack this ability and are unable to comprehend image differentiation. Thanks to the image recognition technology, now we have specialized software and applications that can interpret visual information. An image is composed of picture elements which are known as pixels. Each pixel has finite, discrete quantities of numeric representation for its intensity or grey scale.  So, this means that the computer sees an image as numerical values of these pixels and to recognize a certain image, it must recognize the patterns and regularities in this numerical data [1].

Image recognition generally works with a traditional computer vision approach. Although, this approach is time consuming and requires a lot of expertise. On the other hand, if we use Image recognition with machine learning models it becomes a more efficient and portable approach. The most popular method is deep learning where multiple hidden layers are used in a model [2]. The process of Image recognition begins with training the dataset with training data so that we can create perceptions on how certain classes will look. The images are then fed into a machine learning algorithm. After that AI model testing is done the models are then tested with images that were not part of the training data.

Fig 2. Image Recognition using Machine Learning

Image Recognition with artificial intelligence is used in various industries, below we discuss some prominent applications of AI in Image Recognition [3].

  1. Visual Search

Visual search is a novel technology, powered by AI, that allows the user to perform an online search by employing real-world images as a substitute for text. Google lens is one of the examples of image recognition applications. This technology is particularly used by retailers as they can perceive the context of these images and return personalized and accurate search results to the users based on their interest and behavior. Visual search is different than image search because in visual search we use images to perform searches. In image search, we type the text to perform the search. For example, in visual search, we will input an image of the cat, and the computer will process the image and come out with the description of the image; where in image search, we will type the word “Cat” or “What does a cat look like” and the computer will display images of the cat. Besides Google, many other tech giants are also using image recognition AI technology. The list of these companies includes Snapchat, Pinterest, Microsoft for Bing search, and Amazon.

  1. Image Organization

Currently nearly everyone has access to a smartphone with a camera. People want to capture memorable moments of their lives with their cameras. Hence, there is a greater tendency to snap a large volume of photos and high-quality videos within short periods of time. Taking pictures and recording videos on smartphones is straightforward; however, organizing the volume of content for effortless access for later use is not straightforward. Image recognition AI technology helps to solve this issue by enabling the users to arrange the captured photos and videos into categories that later lead to enhanced accessibility. When the content is organized properly, the users not only get the added benefit of enhanced search and discovery of those pictures and videos, but they can also easily share the content with others. It helps in discovering ideas and themes and begins developing a vision.  This could become one of the major applications of Image Recognition. Google launched a fresh service Google Photos in 2015. It allows users to store unlimited pictures (up to 16 megapixels) and videos (up to 1080p resolution). The service uses AI image recognition technology to analyse the images by detecting people, places, and objects in those pictures, and group together the content with analogous features.

  1. Content Moderation

User-generated content (USG) is the building block of many social media platforms and content sharing communities. These multi-billion-dollar industries thrive on the content created and shared by millions of users. This poses a great challenge of monitoring the content so that it adheres to the community guidelines. It is unfeasible to manually monitor each submission because of the volume of content that is shared every day. Image recognition powered by AI helps with automated content moderation, so that the content shared is safe, meets the community guidelines, and serves the main objective of the platform.

  1. Help Visually Impaired

Visual impairment is a major disability faced by visually challenged people [4]. Today we rely on visual aids such as pictures and videos more than ever for information and entertainment; This has disadvantages for those people who have impaired vision. Since the inception of the internet and social media, users used text-based mechanisms to extract online information or interact with each other. During this time, visually impaired users employed screen readers to comprehend and analyze the information. Now, most of the online content has transformed into a visual-based format, thus making the user experience for people living with an impaired vision or blindness more difficult. Image recognition technology promises to solve the woes of the visually impaired community by providing alternative sensory information, such as sound or touch. One of the early pioneers of this technology is Facebook. It launched a new feature in 2016 known as Automatic Alternative Text for people who are living with blindness or are visually impaired. This feature uses AI-powered image recognition technology to tell these people about the content of the pictures.

Conclusion:

In today’s AI and digital universe, it can be seen how essential image recognition is in its numerous applications that benefit different industries and their workers. Using AI and Image recognition in visual search can help in perceiving context and return more accurate search results. In Image organization we can group the content with similar analogous features. Image recognition powered by AI helps with automated content moderation. Therefore, applications like Visual search, Image Organization, Content Moderation and helping visually impaired thus play a very important role in today’s world. For all of these reasons and more we can now see how and why Image Recognition and AI is such an exciting field.

 

 

 

Reference:

[1] https://www.mygreatlearning.com/blog/image-recognition.html

[2] https://viso.ai/computer-vision/image-recognition.html

[3] https://logicai.io/blog/using-artificial-intelligence-ai-imagerecognition.html

[4] S. M. Felix, S. Kumar and A. Veeramuthu, “A Smart Personal AI Assistant for Visually Impaired People,” 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018, pp. 1245-1250, doi: 10.1109/ICOEI.2018.8553750.