The advent of smartphones, more affordable cameras, and advancements in image recognition through deep learning approaches have ushered in a new era for image recognition. Various industries such as automotive, gaming, and e-commerce are now integrating this technology.
When selecting an image recognition solution, accuracy holds paramount importance. However, depending on the application, continuous learning, speed, and flexibility also become crucial criteria. Established tech giants like Amazon and Google, alongside startups such as Clarifai, are actively providing image recognition services.
What is Image Recognition?
Image recognition involves distinguishing specific objects in a picture through various techniques. It comprises a set of algorithms and methods for labeling and classifying elements within an image. The primary focus is on the content inside the image, and models are trained to assign labels defining the image, drawing inspiration from how animals detect and classify objects.
How is Image Recognition Different from Computer/Machine Vision?
While image recognition and computer/machine vision may seem interconnected, image recognition is a subset of computer vision. Image recognition is dedicated to identifying the content of an image, whereas computer vision encompasses obtaining, describing, and producing results based on the field of application. Image recognition acts as a component of computer vision apps, which, in turn, is a part of machine vision apps. Machine vision involves both hardware and computer vision apps.
How Does Image Recognition Work?
Model training is a crucial step for image recognition, with deep learning methods currently proving to be the most effective tools. The process begins with a dataset, similar to how a newborn learns by being introduced to objects. Machines also require a dataset and, through deep learning techniques, are trained to recognize patterns and boundaries in images. Feature extraction is employed to derive meaningful information from the pixel data of an image. Subsequently, the trained model predicts and labels new input images into specific classes.
[Flowchart of how image recognition works – Source: MathWorks]
For tailored data collection or harvesting services, explore our data-driven list to find the option best suited to your project needs.
To delve deeper into data collection, feel free to download our whitepaper:
[Get Data Collection Whitepaper]
Why is Image Recognition Apps Relevant Now?
The ability of image recognition to extract meaningful data from images has led to numerous applications. Its relevance has surged in the past decade, driven by the increased effectiveness of deep learning and the proliferation of images due to the reduced size and cost of cameras, coupled with widespread smartphone use and image-centric social media platforms.
[Bar chart showing the market increase of image recognition tech]
Image Recognition Use Cases:
- Automotive Industry:
- Crucial for self-driving cars, utilizing multiple cameras and LIDAR for image creation and recognition of traffic lights, vehicles, and other objects.
- Security Industry:
- Enables the detection and identification of faces, enhancing the capabilities of smart home security systems.
- Healthcare:
- Used in detecting tumors, brain strokes, and assisting visually impaired individuals. Algorithms powered by image recognition show high accuracy in identifying lung cancers.
- Retail:
- Facilitates virtual mirror technology, allowing customers at stores like Topshop and Timberland to visualize clothing without trying them on.
- Visual Search and E-commerce:
- Integration with online shopping, contributing to the expected growth of the visual search market.
- Marketing:
- Analyzing image data on social networks for understanding customer preferences and enhancing customized marketing strategies.
[Image of a phone in a hand – Source: Techcrunch]
Things to Consider When Choosing Image Recognition Solutions:
The primary criterion is accuracy, followed by considerations like speed and flexibility in most applications. Evaluating machine learning solutions involves comparing different options based on labeled test datasets. While in-house data-trained solutions often outperform pre-trained ones, the decision depends on achieving the required accuracy level and associated costs.
Users should avoid making generalizations based on a single test, as the effectiveness of an image recognition solution varies with the specific application. Additional criteria include continuous learning, speed, adaptability for future needs, and simplicity of setup and integration.
Image Recognition Apps Vendors:
Rather than building and training a neural network model from scratch, businesses can leverage image recognition technology through APIs offered by tech giants and startups. Open-source APIs are also available for building or improving image recognition systems.
Explore our sortable list of leading image recognition providers, including industry-specific vendors like Visenze, providing solutions for visual search, product tagging, and recommendation.
Notable Image Recognition Apps Vendors:
- Amazon Rekognition
- Clarifai
- Google Cloud Vision API
- IBM Watson Visual Recognition
- Microsoft Azure:
-
- Face API
- Emotion API
- Computer Vision API
- Video API
- OpenCV
- SimpleCV
- Scikit-image
These vendors offer diverse solutions, from cloud-based APIs to open-source libraries, catering to different business needs and preferences.