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“Image recognition” is the ability to identify, recognize and distinguish images and assign them to categories. In humans, this cognitive function is trained unconsciously since our brains have been exposed to the same set of images. This led to a seemingly innate ability to effortlessly pick up visual stimuli and identify & differentiate between things. Contrary to the human brain, Image Recognition, in the context of machine learning is the ability to identify objects using an array of numerical values. It is a coalescence of computer vision and artificial intelligence that translates to the ability of software to identify objects, places, people, logos, buildings, and several other variables in a digital image.
Machines cannot naturally know and identify objects that they see. They can only recognize the category of objects that the engineers programmed into them. Hence, if a machine is not trained to recognize more than one category of images, it will not be able to respond to anything that falls outside of the training spectrum.
The concept of Image Recognition is reliant on the fact that machines view images as a calculated concentration of data, an array of pixel values. Each pixel carries information about the RGB, i.e, Red, Green & Blue color values. In the case of Black & White images, the pixels will carry information about the darkness & the whiteness values.
The process of image recognition involves the formation of a neural network, just like the one in human brains. This neural network is responsible for processing the individual pixels of an image. These neural networks are fed with and exposed to a myriad of pre-labeled images in order to “train” them to recognize similar images.
With the involvement of words like Artificial intelligence, Computer Vision, Machine Learning, etc., understanding the concepts and processes that go behind creating this unique ability of machines to interact with images becomes intimidating. Here’s a quick, flowchart of all the things & processes that are required to fetch successful image recognition results.
Typically, the neural network used for the task is a Convolutional Neural Network. They consist of convolutional and pooling layers along with MLP (Multi Perceptron) layers.
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Images or videos captured using a camera lens at any location
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Trained algorithms that mimic the human brain to interpret images to decode, identify and classify their content accurately
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Provide comprehensive data for our better understanding
The Image Recognition market is growing at a swift pace and is predicted to reach USD 58920 Million by 2026 at a CAGR of 15.6% over the next years. The principal application of Image Recognition technology includes facial recognition, visual geolocation, object tracking, industrial automation, gesture recognition, visual data interpretation, medical assistance, etc. The range and versatility of usages allow this technology to facilitate functions across different industries.
The CPG industry is a chain of closely knit but far-trajectory operations.
With advanced Image Recognition technology, CPG brands can now have real-time access to their product performance. Furthermore, the “click and go” feature by Infilect enables them to keep a close eye on all the following metrics in real-time:
A comprehensive visual intelligence platform for the most accurate, real-time in-store execution insights.
Capturing of high quality in-store images by on-field merchandisers.
Accurately identifies SKUs and display promotions from the images captured from every store.
Critical and actionable execution insights are made available on the InfiViz Dashboard and shared using trackers directly into stakeholder inbox.
Execution insights and targeted action plans are available to on-field merchandisers within 60 seconds.
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