AI Visual Content Analysis Tool

What is coactive.ai?

Coactive.ai is a specialized data analytics platform designed to handle unstructured visual data, including images and videos. It is geared towards data practitioners such as data scientists and machine learning engineers, providing them with the necessary tools to derive meaningful insights from visual content on a large scale. The platform includes features like intelligent search and analytical queries, which facilitate the efficient management and analysis of extensive visual data repositories.

How does Coactive.ai handle privacy and security?

Coactive.ai emphasizes robust privacy and security measures to safeguard user data. The platform has a detailed privacy policy that explains the collection, usage, sharing, and processing of personal information. This policy specifies the types of data gathered, such as device and usage information, along with data from cookies and similar technologies.

The types of personal information collected include users' names, email addresses, phone numbers, and the content of any messages or attachments sent through the platform. Users of the trial version of their image and video search service may also have their search activities within the service recorded.

For users outside the United States, using Coactive.ai entails consenting to the transfer of their personal information to the U.S. for storage and processing.

From a security standpoint, Coactive.ai is SOC2 certified, demonstrating adherence to high standards in security, availability, and confidentiality. The platform also offers contracts to meet additional compliance requirements, underscoring its commitment to enterprise-level security.

Coactive.ai utilizes technologies such as cookies and pixel tags to collect information about users' online activities over time and across different services, with options available for users to adjust their cookie settings via their web browser. These practices are part of Coactive.ai’s commitment to protecting user data while providing a secure environment for visual data analytics.

What are the limitations of coactive.ai?

Coactive.ai offers a suite of powerful tools for analyzing unstructured visual data, but like any technology, it has its limitations. Two primary challenges are associated with its current capabilities:

Incomplete Metadata:
The platform often encounters issues with metadata associated with images and videos. Metadata can be incomplete, inaccurate, or entirely absent, which complicates the analysis. Traditional visual data might not include detailed information about the context, objects, or events depicted, making it difficult to derive meaningful insights without substantial supplementary data.

Management of Large Data Volumes:
Visual data tends to be voluminous and managing such large datasets efficiently is a significant challenge. Processing this kind of unstructured visual content demands substantial computational resources and can be time-consuming, affecting the speed and efficiency of data handling.

Despite these challenges, Coactive.ai is committed to overcoming these hurdles by continually enhancing its platform. It provides specialized tools that enable data practitioners to quickly extract insights from large volumes of unstructured data. Features such as intelligent search and production analytics are designed to help users unlock valuable information from visual content, addressing both the scale and complexity of the tasks involved.

What are the system requirements for using Coactive.ai?

To effectively utilize Coactive.ai, users must satisfy certain system requirements essential for its operation:

Python Environment: Coactive.ai offers an SDK that requires installation within a local Python environment. This is necessary for integrating and utilizing the platform's capabilities through coding.

Authentication Credentials: Access to Coactive.ai's features is secured with environment-specific authentication credentials. Users must obtain these credentials to interact with the platform's APIs effectively.

Data Access: The platform necessitates access to your visual data, such as images and videos stored in a data lake or similar storage systems. This access allows Coactive.ai to transform unstructured visual data into structured, semantically meaningful representations.

Structured Data View: Coactive.ai provides a structured data view for running real-time queries over visual data. In this view, rows represent visual assets, while columns denote associated metadata, facilitating advanced analytical operations.

Internet Connection: A consistent and stable internet connection is crucial for accessing Coactive.ai’s cloud-based services, which include functionalities like searching, defining visual concepts, and conducting analytics.

Compatible Web Browser: Interaction with Coactive.ai’s web interface requires a compatible web browser. Though specific browser requirements are not detailed, commonly used modern browsers such as Chrome, Firefox, or Safari are typically compatible.

Minimum Age Requirement: Users must be at least 18 years old to use the Coactive.ai platform, in accordance with their terms of use.

For the most accurate and comprehensive system requirements, prospective users should refer to Coactive.ai’s official documentation or directly contact their support team for assistance tailored to specific needs and use cases.

How to get started with coactive.ai?

To begin using Coactive.ai, you should follow a structured approach to set up and integrate their tools into your system. Here’s a step-by-step guide to get you started:

1. Install the Coactive SDK:
  - Start by requesting a demo to obtain access to the Coactive platform.
  - Once access is granted, download the latest SDK. Install it in your local Python environment with the following command:
   ```
   pip install coactive_sdk/python/coactive-py3.whl
   ```

2. Authenticate:
  - Load the necessary authentication credentials to call the Coactive APIs. These credentials are specific to your environment and will be provided by Coactive. Here’s an example of setting up authentication:
   ```python
   import coactive
   from auth_credentials import COACTIVE_HOST, CLIENT_ID, CLIENT_SECRET

   access_token = f"{CLIENT_ID}:{CLIENT_SECRET}"
   configuration = coactive.Configuration(
     host=COACTIVE_HOST,
     access_token=access_token,
   )
   ```

3. Initialize a Dataset on Coactive:
  - Grant Coactive access to your visual data. Below is a code snippet to create a dataset named “visual_data”:
   ```python
   from coactive.apis import DatasetApi
   from coactive.model.create_dataset_request import CreateDatasetRequest
   from coactive.model.storage_type_enum import StorageTypeEnum

   with coactive.ApiClient(configuration) as api_client:
     create_dataset_request = CreateDatasetRequest(
       name="visual_data",
       description="A dataset containing images and videos.",
       storage_type=StorageTypeEnum("s3"),
       data_path="s3://coactive-demo-datasets/quickstart/",
     )
     try:
       api_response = DatasetApi(api_client).create_dataset(create_dataset_request)
     except coactive.ApiException as e:
       print(f"Exception when calling DatasetApi: {e}\n")
     dataset_id = api_response.dataset_id
   ```

4. Access Semantically Meaningful Representations:
  - Once Coactive has access to your data, it will transform your unstructured visual data into semantically meaningful representations. You can retrieve additional metadata about these representations with this code:
   ```python
   from coactive.apis import EmbeddingApi

   with coactive.ApiClient(configuration) as api_client:
     try:
       api_response = EmbeddingApi(api_client).get_embeddings_by_dataset_id(dataset_id)
     except coactive.ApiException as e:
       print(f"Exception when calling EmbeddingApi: {e}\n")
     embedding_ids = {emb.name: emb.embedding_id for emb in api_response['data']}
     embedding_id = embedding_ids['multimodal']
   ```

5. Search Your Images:
  - Utilize Coactive’s intelligent search functionality to find visual assets. Here’s how you can perform text-only, image-only, and multimodal searches:
   ```python
   from coactive.apis import SimilaritySearchApi

   text_query = 'a green dress'
   image_query = 'path/to/example_dress.jpg'
   limit = 5

   with coactive.ApiClient(configuration) as api_client:
     try:
       if text_query and not image_query:
         search_response = SimilaritySearchApi(api_client).search_images_by_text_grouped(
           embedding_id=embedding_id,
           query=text_query,
           limit=limit
         )
       elif not text_query and image_query:
         search_response = SimilaritySearchApi(api_client).get_similar_images_grouped_semantic(
           embedding_id=embedding_id,
           file=open(image_query, 'rb'),
           limit=limit
         )
       elif text_query and image_query:
         search_response = SimilaritySearchApi(api_client).get_similar_images_grouped_semantic(
           embedding_id=embedding_id,
           file=open(image_query, 'rb'),
           metadata_value=text_query,
           limit=limit
         )
     except coactive.ApiException as e:
       print(f"Exception when calling SimilaritySearchApi: {e}\n")
     search_result_image_ids = [result['items'][0]['coactive_image_id'] for result in search_response['data']]
   ```

6. Explore Tutorials and Documentation:
  - For more detailed information and tutorials on common use cases, refer to the official Coactive.ai documentation.

Make sure to replace placeholders like COACTIVE_HOST, CLIENT_ID, CLIENT_SECRET, and specific file paths with your actual configuration details and file locations.

How does CoactiveAI enhance metadata generation?

CoactiveAI uses artificial intelligence to automate the metadata generation process for visual content, achieving 95% accuracy. This eliminates the need for manual tagging, making the process faster and more cost-effective. By tuning the model to understand domain-specific contexts, CoactiveAI helps improve the discoverability of assets within media management systems, unlocks valuable digital archives, and creates new revenue-generating opportunities.

Who can benefit from using CoactiveAI?

CoactiveAI is designed for content optimization and data science teams across various industries, including entertainment, retail, and online communities. Media and entertainment companies can use the platform to unlock and monetize decades of footage. Retail and e-commerce companies can streamline their asset labeling and management processes. Online communities benefit from automated content moderation and the ability to quickly identify and remove objectionable content.

What are the key features of CoactiveAI's Multimodal Application Platform?

CoactiveAI's Multimodal Application Platform combines a range of native capabilities such as a semantic engine and data & AI systems in a cloud-based service. It offers functionalities through both a user interface (UX) and an API, helping data and strategy teams leverage multimodal AI technologies. Features include intelligent search, dynamic tagging, and real-time analytics, enabling users to optimize their visual data management and uncover business value more efficiently.