More than two billion pictures are shared each day in social networks. Research demonstrates it might take a person ten years to look at all the photographs shared on Snapchat at the previous hour! That is quite a tedious task and well beyond human capacities.
Media buyers and providers experience difficulty organizing applicable content in groups, parsing components of images/videos, and specifying that the yield on investment out of generated articles in an efficient manner. Obtaining insights (i.e. metadata, article, categories, color, etc) from rich media -- quickly, accurately, and mechanically -- has long been a challenge.
Here are Major Terminologies relevant to Image Recognition:
- Machine learning (ML) is a guided approach to attain artificial intelligence. It allows us to espouse intelligence with calculations and is a buzzword that people hear every day. ML utilizes algorithms to parse data, learn from it, and even help predict outcomes.
- Deep learning, a subset of ML or a technique to implement ML, is rapidly taking over sectors as developers utilize the ability of neural systems to detect insights. Automatically detecting patterns without human hand-coding features is the new holy grail.
- Computer Vision enables computers to spot images, using sensors and image processors to match individual eyes' capacities. There's a slight overlap between ML and CV (see diagram above). CV has become important as it helps process the astounding number of visual imagery created every day.
- Image processing is a method to do some operations (improvement or compression) in an image to extract valuable information. ML can be used in both the computer vision and picture processing.
- Image recognition is just another term to articulate the procedure for identifying and detecting an item or a feature in videos or images. There are people who believe computer vision exactly like picture recognition, but personal computer vision is significantly broader and includes objective recognition, character recognition, and text/sentiment analysis. It is normal to associate picture recognition with facial discovery, but there's more to it. As an Example, here are some of the major attributes that a pioneer ecosystem in offering a deep learning powered image recognition and image processing solution provides:
- Classify/categorize scenes - automatically categorize photos; for instance, sorting our personal photos. beach vs. furry etc.. However, one of the stronger capabilities of Imagga is to prepare a particular classifier for a specific use case and perpendicular, ranging from personal photographs to plant identification to sorting junk.
- Visual similarity search - infusion signatures of the images in a collection that allows search among them based on visual and/or semantic similarity in the future, as well as similar photos/products suggests.
- Realize that the main object - identify the principal object in a picture.
- Facial recognition faces - detect faces and bunch them in virtual persons, that can be used for better organization of private photos and societal media observation.
- Auto-tag photos - tag many items with different key phrases to assist sorting photographs and/or extracting statistics.
- Analyze composition - automatically detect the most visually interesting areas in photographs and eventually enable intelligent cropping of these regions of attention.
- Extract colors - identify representative colors in graphics, including separate recognition of foreground and background colors where required.