Big data analytics and AI importance in media and entertainment industry

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

Which is better for image classification, supervised or unsupervised classification?

Image classification is actually fundamental task that helps to classify and comprehend the entire image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification to images in which only one object appears and that is only analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.

Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools

In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

While on the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.

What is Best Data Labeling Process to Create Training Data for AI?

Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings.

Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.

During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients. The data is encrypted to make sure it can be safely delver to the clients to avoid any risk. So, right here we will discuss about the data labeling process to step wise facts.

DATA LABELING PROCESS

Collection of Datasets

The first step towards data annotation is understand the problem to provide the precise training data. Hence, collecting the datasets from client is an important aspect. So, the raw data is collected directly from the client in the well-organized format.

Also Read : How to Create Training Data for Machine Learning?

The data is collected through a proper channel to make sure its originality and security. Many business enterprises follow the different routes to send the data for labeling. Sometimes it is supplied in encrypted format and after data annotation it is again sent to client in the secured format.

Labeling of Dataset

After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for the supervised machine learning labeled data is required, and proper labeling is important to make sure AI model get trained precisely and work in the right manner.

Choosing the right tools and technique is another factor for data labeling. And in image annotation is done to create the training data sets for computer vision based AI model. The quality is also need to be ensured to make sure the model can predict with the accurate results. To consider all these points two points also need to discussed here – how to label data and who will label the data.

How to Label Data: After getting the data set for labeling, the annotation team has to decide the type of annotation applied here, like detecting, classifying and segmentation of the object. Here if client provides the specific tool or software, then annotators use to annotate the images using the same.

Once the data sets are assigned to annotators and instructed what type of annotation and what are the tools will be best suitable to annotate the data.

Who Will Label the Data: Similarly, the next step into data labeling process comes, who will annotate or label the data. Here, two options are available for the AI companies – first organize the in-house data labeling facility which could be easy control for you and might cost less but it can take extraordinary time due to collection and labeling of entire data sets.

The second option is outsource the labeling task to other data annotation companies, who have team of well-trained and experienced annotators to label the data for machine learning with better efficiency and quality. The best part of outsourcing is data has the ability to aggregate quickly. While on the other hand transparency, accuracy and high-cost are the concerning factors with outsourcing services.

Quality Check and Evaluation

After annotating the data, checking the quality is one of the most important factors of data labeling process. Here, qualified annotator manually check the quality of each annotated images to make sure machine learning algorithm get trained with right accuracy.

Also Read : How to Build Training Data for Computer Vision?

Here, the data sets are also evaluated to validate the same, and if there is any correction the data is annotated correctly and finally validated for machine learning training. Here highly experienced, annotators are required to prudently the check the quality of data labeled to make sure AI companies get the best and high-quality datasets at best pricing.

Final Delivery of Annotated Datasets

The last step in data annotation process is after labeling, the data need to be safety delivered to client. Here again the authenticity and privacy of data is ensured till the data is delivered to client. And the mode of delivering the data also depends on the company to company but there should be safe mode to send such data with complete confidentiality and safety.

Data Labeling Process at Cogito

Most of the companies follow the above discussed data labeling process but few companies have more complex or even more sophisticated but secured data annotation process. Cogito is one the companies providing the world-class data labeling solution with next level of accuracy. It is following the international standards for data security and privacy to ensure the originality of AI model.

Hire Machine Learning Engineer and Data Scientist for AI Models Development

To develop the AI models machine learning engineers required with lots of training data sets. And to analyze the data for machine learning and AI, data scientists are required. Hiring the right data scientist and machine learning engineers are difficult for the AI companies. And if the project is on temporary or contract basis a suitable machine learning engineer is required for AI developments.

Continue reading “Hire Machine Learning Engineer and Data Scientist for AI Models Development”

Why Machine Learning Model Validation in Important for AI Models?

Validating the machine learning model outputs are important to ensure its accuracy. Basically, when machine learning model is trained, (visual perception model), there are huge amount of training data sets are used and the main motive of checking and validating the model validation provides an opportunity to machine learning engineers to improve the data quality and quantity. 

Continue reading “Why Machine Learning Model Validation in Important for AI Models?”

Training Data for AI and Machine Learning in Insurance Claim

AI in insurance helping to detect the type and level of damage to vehicles. Actually, the AI-based computer vision technology is used to detect the area of damage and calculate the intensity providing an automatic detail for insurance claim processing. Cogito provides, training data for AI and machine learning in insurance with best level of accuracy for right predictions.

Continue reading “Training Data for AI and Machine Learning in Insurance Claim”

Artificial Intelligence in Urban Management

AI in urban management is becoming possible with well-trained AI system and applications to gather the various details while developing such urban cities. To train the AI models used in urban management a huge quality of training data sets required for machine learning. Cogito provides, the wide ranging image annotation service to annotate the object of interest in the images and making it recognizable to model.     

Continue reading “Artificial Intelligence in Urban Management”

How To Use Machine Learning and AI in Inventory Management?

AI in inventory management helping logistic supply chain and warehousing companies to manage the inventory through fully automation system making the whole process fast and more efficient. With training data sets robots and automated machines can be created to develop such AI models. Cogito provides the high quality training data for AI models used in inventory management.

Continue reading “How To Use Machine Learning and AI in Inventory Management?”

What are the Features of AI Security Camera and How it Works?

AI-enabled cameras nowadays are highly in demand due to advance monitoring system. Compare to old-age security cameras, now thanks to AI technology, cameras are now equipped with more advanced features to provide the more comprehensive detail of surroundings with more clear scenarios.

Continue reading “What are the Features of AI Security Camera and How it Works?”

How is AI Changing Fashion Design or Helping a Clothing Designer?

AI in fashion is playing a crucial role in changing the designs as per the new trend in the market, while keeping in the mind about the current scenario and future prospects. Actually, this technology is equipped with machine learning to understand the historical patterns and buying habits of the customers, and changing the style of fashion with the time being. 

Continue reading “How is AI Changing Fashion Design or Helping a Clothing Designer?”