Why image recognition important?
About 80 percent of the content on the internet is visual. You can already start working out why image tagging might hold its place as king of the content table. Whether it’s individuals or companies, AI image recognition has made it possible to identify visuals online with minimal fuss. There around 657 billion photos posted each year digitally, with the majority appearing on social media. A good chunk of those images are people promoting products, even if they are doing so unwittingly. User-generated content (UGC) in its purest form is an excellent enabler for brands as it provides the best kind of promotion.
There are marketing tools to alert companies when there is a consumer mention on social media, but what about when brands promotion takes place without anyone tagging their name in the social post? This is where AI image recognition proves its value. If the tech is fed the correct datasets, AI can identify an image without specific tag mentions. The results are invaluable for brands to track and trace their social mentions.
How does image recognition work?
As we know AI can search social media platforms looking for photos and compare them to extensive data sets. It then decides on relevant image that matches at a rate much faster than humans are capable of. Brands use image recognition to find content similar to their own on social media. That means identifying a brand’s logo or recognising organically placed product placement amongst social media users. Asking humans to trawl through so much information easily becomes tiring. AI doesn’t worry about the human error, and returns precise results at unparalleled levels. AI image recognition monitors what people are saying about a brand without the need for text. Brands able to track their social mentions without users needing to type the company name will find themselves in an advantageous position. The potential to tap into their own online coverage solely through AI recognised identifiers is huge and offers unparalleled coverage.
Here is some typical tasks of image recognition:-
At first we have to determine whether or not the image data contains some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case: arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can be best solve only for specific objects, such as simple geometric objects (e.g., polyhedra), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera. Different varieties of the recognition problem are described in the literature:
• Object recognition
One or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene.
An individual instance of an object is recognized. Examples are identification of a specific person’s face or fingerprint, or identification of a specific vehicle.
The image data is scanned for a specific condition. Examples are detection of possible abnormal cells or tissues in medical images or detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data which can be further analyzed by more computationally demanding techniques to produce a correct interpretation.
Several specialized tasks based on recognition exist, such as:
• Content-based image retrieval
Here finding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative a target image (give me all images similar to image X), or in terms of high-level search criteria given as text input (give me all images which contains many houses, are taken during winter, and have no cars in them).
• Pose estimation
we have to estimate the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot retrieving objects from a conveyor belt in an assembly line situation.
• Optical character recognition
OCR which is identifying characters in images of printed or hand written text, usually with a view to encoding the text in a format more and enable to editing or indexing Department of Computer Science and Engineering, Michigan State University. “The Pattern Recognition and Image Processing (PRIP) Lab faculty and students investigate the use of machines to recognize patterns or objects. Methods are developed to sense objects, to discover which of their features distinguish them from others, and to design algorithms which can be used by a machine to do the classification. Important applications include face recognition, fingerprint identification, document image analysis, 3D object model construction, robot navigation, and visualization/exploration of 3D volumetric data. Current research problems include biometric authentication, automatic surveillance and tracking,handless HCI, face modeling, digital watermarking and analyzing structure of online documents. Recent graduates of the lab have worked on handwriting recognition, signature verification, visual learning, and image retrieval.”
⦁ Facial Recognition
we know that face recognition systems are progressively becoming popular as means of extracting biometric information. Face recognition has a critical role in biometric systems and is attractive for numerous applications including visual surveillance and security. Because of the general public acceptance of face images on various documents, face recognition has a great potential to become the next generation biometric technology of choice.
Image Recognition Systems
⦁ Motion analysis
Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images . Examples of such tasks are:
⦁ Ego motion
Determining the 3D rigid motion (rotation and translation) of the camera from an image sequence produced by the camera.
Tracking is following the movements of a (usually) smaller set of interest points or objects (e.g., vehicles or humans) in the image sequence.
⦁ Optical flow
This is to determine, for each point in the image, how that point is moving relative to the image plane, i.e., its apparent motion. This motion is a result both of how the corresponding 3D point is moving in the scene and how the camera is moving relative to the scene.
⦁ Scene reconstruction
Given one or (typically) more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene. In the simplest case the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model
⦁ Image restoration
The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look like, a model which distinguishes them from the noise. By first analysing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches. An example in this field is their painting. Some systems are stand-alone applications which solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on if its functionality is pre-specified or if some part of it can be learned or modified during operation. There are, however, typical functions which are found in many computer vision systems.
Deeper learning with image recognition
Image recognition was around before AI. Yet the machine learning factor is revolutionizing methods for identifying an object or person’s face. Machine learning is only effective when there is data to feed it, however. For all of AI’s automation, tasking it to identify images is not a simple request. Our understanding of visuals is second nature; it’s something we are programmed to do from a young age. Asking the same of a machine isn’t a straightforward process. For that reason, one of the more popular forms of AI recognition is convolutional neural networks (CNN). CNN is a method that focuses on pixels located next to each other. Closely-located images are more likely to be related, which means an object or face is matched to a picture with more transparency.
While brands looking to monetize social media though AI image recognition carry clear benefits, its use cases run far deeper. Self-driving cars are about to be the next big thing in the automobile world, and AI image recognition tech is helping to power them. A self-driving car that can detect objects and people on the road so it doesn’t crash into them doesn’t happen automatically. It needs to recognize the images to make informed decisions. Each self-driving car is fitted with several sensors so it can identify other moving vehicles, cyclists, people – basically anything that could pose a danger. An automated car needs to process the hazards of the road the same way a seasoned driver does. There are still a few aspects to iron out before self-driving cars hit the road in 2020. But when vehicle automation does kick-in, AI image recognition will be one of the major drivers behind them working safely.
A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images), but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or nuclear magnetic resonance.
Before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to assure that it satisfies certain assumptions implied by the method. Examples are
1. Re-sampling in order to assure that the image coordinate system is correct.
2. Noise reduction in order to assure that sensor noise does not introduce false information.
3. Contrast enhancement to assure that relevant information can be detected.
4. Scale-space representation to enhance image structures at locally appropriate scales.
⦁ Feature extraction:
Image features at various levels of complexity are extracted from the image data. Typical examples of such features are lines, edges and ridges
Localized interest points such as corners, blobs or points. More complex features may be related to texture, shape or motion.
At some point in the processing a decision ismade about which image points or regions of the image are relevant for further processing. Examples are
1. Selection of a specific set of interest points
2. Segmentation of one or multiple image regions which contain a specific object of interest.
⦁ High-level processing:
At this step the input is typically a small set of data, for example a set of points or animage region which is assumed to contain a specific object. The remaining processingdeals with, for example:
1. Verification that the data satisfy model-based and application specificassumptions.
2. Estimation of application specific parameters, such as object pose or objectsize.
3. Classifying a detected object into different categories.So, image processing help AI to identify the image and respond according to the image identification.
A seamless future of imagery
As the tech improves, image recognition will return even greater results. Head of Machine Learning at Lobster, Vladimir Pavlov says, “The mathematical basis for object recognition has existed for a long time, but technological possibilities of using computer vision algorithms appeared recently. Already, neural networks allow making perfect detectors that are capable of working better than humans. A big jerk holds back the presence of marked image datasets for training, but in the near future, this will not be a problem. Computer vision engineers are actively working on self-learning algorithms”.With a future so heavily influenced by visual communication, image recognition is going to be the key factor behind many of the pictures we see. Both in real life and online.