Semantic Video Search: Unlocking Visual Content

Video content is exploding in volume. From social media platforms to corporate training materials, videos are everywhere. But with this volume comes challenge: how do we search, retrieve, and analyze this sea of visuals? Semantic video search, enables us to understand and interact with video content.

What is Semantic Video Search?

Semantic video search is an AI-powered technology that enables users to search and retrieve video content based on its meaning and context, rather than just relying on text-based metadata or tags. It uses machine learning and natural language processing to analyze and understand the visual and auditory elements within videos, making it possible to find relevant content quickly and accurately.

For Non-Technical Audiences: Transforming Video Discovery

Use Cases and Benefits

Content Libraries: Media companies can organize and retrieve footage from vast archives with unprecedented ease.
E-learning Platforms: Students can find specific topics within long lecture videos instantly.
Social Media Monitoring: Brands can track their visual presence across platforms without relying on text descriptions.
Security and Surveillance: Law enforcement can quickly locate specific events or objects in hours of footage.

ROI and Quantifiable Outcomes

Time Savings: Reduce search time by up to 90% compared to manual methods.
Improved Accuracy: Increase relevant content discovery by 75% over traditional keyword searches.
Enhanced User Experience: Boost user engagement by 50% through more intuitive and effective video navigation.
Cost Reduction: Cut labor costs associated with manual video tagging and cataloging by 60%.

How It Works (Simplified)

Video Analysis: AI breaks down the video into scenes and analyzes visual elements, speech, and text.
Understanding Context: The system learns to understand the meaning and relationships within the content.
Smart Indexing: Videos are indexed based on their semantic content, not just keywords.
Intelligent Search: Users can search using natural language or even by uploading a sample video clip.

This enables us to perform text searches like "two people inside a car" and return the exact timestamp:

For Technical Audiences: Building Semantic Video Search

Core Technologies

Computer Vision: For object detection, scene segmentation, and action recognition.
Natural Language Processing: To understand and process text queries and speech within videos.
Machine Learning: For training models to recognize patterns and context in visual data.
Vector Embeddings: To represent video content in a format suitable for semantic comparison.

Architecture Overview

graph TD
    A[Video Input] --> B[Frame Extraction]
    B --> C[Feature Extraction]
    C --> D[Embedding Generation]
    D --> E[Vector Database]
    F[User Query] --> G[Query Processing]
    G --> H[Vector Search]
    E --> H
    H --> I[Results Ranking]
    I --> J[Search Results]

Implementing Semantic Video Search with Mixpeek's Multimodal SDK

Mixpeek's Multimodal SDK simplifies the process of building a semantic video search system. Here's how you can get started:

Installation and Setup

pip install mixpeek

Initialize the Mixpeek Client

from mixpeek import Mixpeek

client = Mixpeek(api_key="your_api_key_here")

Process and Index Videos

def index_video(video_url, collection_id):
    response = client.index.url(
        url=video_url,
        collection_id=collection_id,
        settings={"video": {"transcribe": True}}
    )
    return response

# Index a video
index_video("https://example.com/sample_video.mp4", "my_video_collection")

Perform Semantic Search

def semantic_video_search(query, collection_id):
    response = client.search.text(
        input=query,
        modality="video",
        input_type="text",
        collection_id=collection_id,
        page=1,
        page_size=10
    )
    return response

# Search for videos
results = semantic_video_search("people playing basketball", "my_video_collection")

Advanced Features: Video-to-Video Search

Mixpeek also supports video-based queries, allowing users to find similar videos or specific scenes:

def video_to_video_search(video_file, collection_id):
    with open(video_file, "rb") as file:
        response = client.search.upload(
            file=file,
            collection_id=collection_id,
            page=1,
            page_size=10
        )
    return response

# Search using a video file
results = video_to_video_search("query_video.mp4", "my_video_collection")

Getting Started with Mixpeek

As a developer, when you create an account on mixpeek.com/start, you'll get access to a powerful set of tools to jumpstart your development process:

Interactive Jupyter Notebook: Upon signup, you'll receive access to a comprehensive Jupyter notebook. This notebook is designed to:
- Walk you through all the API methods available in the Mixpeek Multimodal SDK.
- Provide hands-on examples for each method.
- Allow you to experiment with the API in a sandboxed environment.
Starter Content: Your account will be automatically populated with:
- A selection of starter videos
- A variety of sample images

This starter content serves multiple purposes:

It allows you to immediately start experimenting with semantic search queries.
You can use this content to test different indexing and search strategies.
It provides a baseline for understanding how the API handles different types of visual content.

Rapid Prototyping: With the combination of the interactive notebook and pre-loaded content, you can quickly:
- Prototype your semantic video search application.
- Understand the capabilities and limitations of the API.
- Refine your approach before integrating Mixpeek into your production environment.

Sample API Responses

To give you a better understanding of how Mixpeek's Multimodal SDK works, let's look at sample responses for both search and index operations.

Sample Search Response

When you perform a search operation, you'll receive a JSON response similar to this:

{
    "results": [
        {
            "created_at": "2024-08-29T17:04:56.159000",
            "caption": "there is a man in a blue bathing suit posing by a pool",
            "file_id": "880ce6ec-98cb-4254-ae80-a4bd39769b90",
            "collection_id": "starter",
            "metadata": {},
            "url": "...",
            "score": 0.6397992968559265
        },
        // ... more results ...
    ],
    "pagination": {
        "total": 100,
        "page": 1,
        "page_size": 10,
        "total_pages": 10,
        "next_page": "https://api.mixpeek.com/search/text?page=2&page_size=10",
        "previous_page": null
    }
}

This response includes:

An array of results, each containing detailed information about the matched content.
A pagination object for handling large result sets.

Sample Index Response

When you initiate an indexing operation, you'll receive an asynchronous response:

{
    "message": "URL content processing started",
    "task_id": "16653ec7-edc6-4055-88f5-c2a447eedc1b"
}

Note that indexing is an asynchronous operation run in a distributed queue via Temporal. The task_id can be used to check the status of the indexing process later.

Handling Asynchronous Indexing

To handle the asynchronous nature of indexing, you can implement a status check mechanism:

def check_indexing_status(task_id):
    status = client.index.get_status(task_id)
    return status

# Example usage
task_id = "16653ec7-edc6-4055-88f5-c2a447eedc1b"
status = check_indexing_status(task_id)
print(f"Indexing status: {status}")

This approach allows you to build robust applications that can handle large-scale video indexing without blocking operations.