About Stock Photo Data Licensing
Recently, the famous stock photo-selling platform Shutterstock has introduced a new form of Licensing: Data Licensing. The photos categorized under this form of Licensing are not eligible to be sold in the open market. This means these images are not getting listed in search results on the seller platform. This has created a huge amount of confusion. With very limited information available on the internet, contributors are unaware of how they will benefit from this Data Licensing.
Shutterstock introduced the concept of Data licensing after it announced its partnership with AI giant Openai (the company that owns ChatGPT). Under this partnership, OpenAI will be using, Shutterstock’s image libraries to train its Artificial Intelligent system which will be used for various purposes like automated image tagging, AI image generation, Automated Copyright detection, Self-driving cars, etc.
In this article, I will try to explain, how artificial intelligent systems will use our images sold under data licensing, and how contributors will benefit from all this. I will be explaining in detail about Computer vision, a field in Artificial Intelligence development research, in which photos and visuals are used to train AI systems to see and understand the outer world same as how the human eye and brain do.
Understanding Computer Vision and AI Training
Computer Vision is a field of Artificial Intelligence (AI) that works on making machines interpret visual information from images or videos, similar to human visual perception. Under this field of research, scientists develop algorithms and models that can analyze and extract meaningful data from images such as detecting patterns, recognizing objects and places, etc.
Computer Vision is still being developed and has a vast and diverse area of applications. It is already being used in various fields, including surveillance systems, self-driving cars, robotics, etc. Datasets in the images taken from photo stock platforms like Shutterstock will be utilized in revolutionizing industries by providing enhanced visual analysis and decision-making capabilities.
Datasets are sets of visual content organized by a specific theme or topic that can include images (including photos, illustrations, and vectors), videos, and 3D models. Metadata, including keywords, descriptions, geo-location, and categories associated with an image, vector, and 3D models will be part of the datasets.
AI systems, including computer vision models, are trained using large datasets to learn patterns and make accurate predictions. Stock photo datasets are valuable resources for training computer vision models, providing diverse and labeled visual data that can help AI systems understand and interpret various visual concepts.
Training AI Systems with Stock Photo Datasets
- Image Classification: When we upload any image to a stock photo site, we add certain descriptions and keywords related to that image. We also tag a geographic location where that image was taken. All this access information makes a buyer search and understands the contributor’s point of view when he/she has taken the image. Thus, helps the buyer to classify the image into certain categories, like landscape, food, country of origin, etc. Similar to this, scientists are using Datasets obtained from these images to train computer vision models. This will help the AI system to learn to recognize and differentiate differences between objects, scenes, or concepts.
- Object Detection and Localization: Stock photo datasets are instrumental in training AI systems to detect and localize objects within images. Through bounding box annotations or pixel-level segmentation, contributors’ images help train computer vision models to identify and locate specific objects, enhancing their understanding of object boundaries and spatial relationships. for example, if a photo of a restaurant is given to an AI system, it should be trained with the help of various datasets, to identify furniture apart from people or it should tell what kind of food is being served. It should be able to identify the objects used for decoration like flowers, wall hanging, glassware, etc.
- Semantic Segmentation: Semantic segmentation means, in an image each pixel is labeled corresponding to a class or category. Using datasets from the photos, these labeled categories help AI systems understand the boundaries and Semantics of different objects, thus a more precise segmentation is enabled in computer vision.
Earning Opportunities for Stock Photo Contributors in Computer Vision Research
- Collaborations with AI Researchers: Many big organizations and research centers that are working on AI development openly invite photo contributors who can partner with researchers. The contributor has to submit specific types of images for their projects, contributors can provide unique and relevant content that aligns with the researchers’ objectives. These collaborations can result in financial compensation and recognition for the contributors’ valuable contributions to the research community.
- Licensing and Royalties: Stock photo contributors can also earn through licensing their images for commercial use. As AI systems and computer vision applications are becoming popular, the demand for high-quality, diverse, and licensed images is increasing. By licensing their photos through stock photo platforms, contributors can earn royalties each time their images are used in AI applications, such as computer vision models or related products.
With the growing demand for automation in every field, Artificial Intelligent systems are the future. Thus, it’s high time for photo contributors to explore new opportunities to harness this new form of earning in the field of Artificial intelligence and also be a part of this technological revolution.
Similar read : Shutterstock’s Data Licensing: How It Benefits Contributor?
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