Global Image Recognition in CPG Market 2025 by Company, Regions, Type and Application, Forecast to 2031
Description
According to our (Global Info Research) latest study, the global Image Recognition in CPG market size was valued at US$ 905 million in 2024 and is forecast to a readjusted size of USD 1142 million by 2031 with a CAGR of 3.4% during review period.
Computer technology that uses algorithms and machine learning to identify certain humans, animals, objects or other different topics is called image recognition. It is connected to computer vision and can recognize people and other things. Machines that actively process image data are called image recognition machines. It can be done in many ways, but convolutional neural network is one of the most popular methods to filter images through a series of artificial neuron layers. Convolutional neural networks are specially constructed for recognition and similar imaging. Convolutional neural filters use various techniques, such as maximum packing, phase setting, and filling, to create images to help identify topics in machine learning systems. Photos are treated as gratings and images are treated as vectors by the machine. Raster image refers to a series of color pixels, while vector image is a collection of color labeled polygons.
Computer technology that uses algorithms and machine learning to identify certain humans, animals, objects or other different topics is called image recognition. It is connected to computer vision and can recognize people and other things. Machines that actively process image data are called image recognition machines. It can be done in many ways, but convolutional neural network is one of the most popular methods to filter images through a series of artificial neuron layers. Convolutional neural networks are specially constructed for recognition and similar imaging. Convolutional neural filters use various techniques, such as maximum packing, phase setting, and filling, to create images to help identify topics in machine learning systems. Photos are treated as gratings and images are treated as vectors by the machine. Raster image refers to a series of color pixels, while vector image is a collection of color labeled polygons.
This report is a detailed and comprehensive analysis for global Image Recognition in CPG market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Image Recognition in CPG market size and forecasts, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market size and forecasts by region and country, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market size and forecasts, by Type and by Application, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market shares of main players, in revenue ($ Million), 2020-2025
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Image Recognition in CPG
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Image Recognition in CPG market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include IBM, Google, Qualcomm, Microsoft, AWS, Trax, Catchoom, Slyce, LTU Tech, Imagga, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Image Recognition in CPG market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Cloud Based
Local Deployment
Market segment by Application
Inventory analysis
Category Analysis
Product and Shelf Monitoring Analysis
Others
Market segment by players, this report covers
IBM
Google
Qualcomm
Microsoft
AWS
Trax
Catchoom
Slyce
LTU Tech
Imagga
Vispera
Blippar
Ricoh innovations
Clarifai
Deepomatic
Huawei
Honeywell
Toshiba
Oracle
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Image Recognition in CPG product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Image Recognition in CPG, with revenue, gross margin, and global market share of Image Recognition in CPG from 2020 to 2025.
Chapter 3, the Image Recognition in CPG competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2020 to 2031
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2020 to 2025.and Image Recognition in CPG market forecast, by regions, by Type and by Application, with consumption value, from 2026 to 2031.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Image Recognition in CPG.
Chapter 13, to describe Image Recognition in CPG research findings and conclusion.
Computer technology that uses algorithms and machine learning to identify certain humans, animals, objects or other different topics is called image recognition. It is connected to computer vision and can recognize people and other things. Machines that actively process image data are called image recognition machines. It can be done in many ways, but convolutional neural network is one of the most popular methods to filter images through a series of artificial neuron layers. Convolutional neural networks are specially constructed for recognition and similar imaging. Convolutional neural filters use various techniques, such as maximum packing, phase setting, and filling, to create images to help identify topics in machine learning systems. Photos are treated as gratings and images are treated as vectors by the machine. Raster image refers to a series of color pixels, while vector image is a collection of color labeled polygons.
Computer technology that uses algorithms and machine learning to identify certain humans, animals, objects or other different topics is called image recognition. It is connected to computer vision and can recognize people and other things. Machines that actively process image data are called image recognition machines. It can be done in many ways, but convolutional neural network is one of the most popular methods to filter images through a series of artificial neuron layers. Convolutional neural networks are specially constructed for recognition and similar imaging. Convolutional neural filters use various techniques, such as maximum packing, phase setting, and filling, to create images to help identify topics in machine learning systems. Photos are treated as gratings and images are treated as vectors by the machine. Raster image refers to a series of color pixels, while vector image is a collection of color labeled polygons.
This report is a detailed and comprehensive analysis for global Image Recognition in CPG market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Image Recognition in CPG market size and forecasts, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market size and forecasts by region and country, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market size and forecasts, by Type and by Application, in consumption value ($ Million), 2020-2031
Global Image Recognition in CPG market shares of main players, in revenue ($ Million), 2020-2025
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Image Recognition in CPG
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Image Recognition in CPG market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include IBM, Google, Qualcomm, Microsoft, AWS, Trax, Catchoom, Slyce, LTU Tech, Imagga, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Image Recognition in CPG market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Cloud Based
Local Deployment
Market segment by Application
Inventory analysis
Category Analysis
Product and Shelf Monitoring Analysis
Others
Market segment by players, this report covers
IBM
Qualcomm
Microsoft
AWS
Trax
Catchoom
Slyce
LTU Tech
Imagga
Vispera
Blippar
Ricoh innovations
Clarifai
Deepomatic
Huawei
Honeywell
Toshiba
Oracle
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Image Recognition in CPG product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Image Recognition in CPG, with revenue, gross margin, and global market share of Image Recognition in CPG from 2020 to 2025.
Chapter 3, the Image Recognition in CPG competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2020 to 2031
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2020 to 2025.and Image Recognition in CPG market forecast, by regions, by Type and by Application, with consumption value, from 2026 to 2031.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Image Recognition in CPG.
Chapter 13, to describe Image Recognition in CPG research findings and conclusion.
Table of Contents
130 Pages
- 1 Market Overview
- 2 Company Profiles
- 3 Market Competition, by Players
- 4 Market Size Segment by Type
- 5 Market Size Segment by Application
- 6 North America
- 7 Europe
- 8 Asia-Pacific
- 9 South America
- 10 Middle East & Africa
- 11 Market Dynamics
- 12 Industry Chain Analysis
- 13 Research Findings and Conclusion
- 14 Appendix
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