Search videos semantically: Workflow and applications

September 13, 2024
6 Mins
In-Video AI
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Imagine this: You’re watching a 90-minute documentary, trying to find the exact moment where a scientist explains the concept of black holes. You don't remember when it happens, so you start scrubbing through the video, hoping to stumble upon the right part. After a frustrating 10 minutes, you still haven't found it.

Now, imagine a different scenario. You type "the part where they talk about black holes" into a search bar, and instantly, the video jumps to that exact moment. No more guesswork, no more wasted time. That's how semantic video search works.

How semantic video search works


Semantic video search helps you find meaningful parts of a video based on the content's actual meaning rather than just matching keywords or phrases.  

In this blog, we’ll explore semantic video search and how it's changing how we find information in videos. We'll look at how it works, how it differs from traditional search methods, and real-life examples of its use. By the end, you’ll see how this technology can save you time, improve search results, and make video content easier to navigate.

What is semantic search?

Semantic video search is an advanced technology that enables users to find specific content within videos based on the meaning and context of their query, rather than just matching exact phrases. Instead of manually scrubbing through hours of footage, semantic video search allows you to search for video segments based on concepts or themes.

For instance, if you’re looking for a particular scene in a movie, such as a dramatic argument or a cooking demonstration, you can type a query like “heated argument” or “recipe for chocolate cake.” The semantic video search engine will understand these terms' meanings and locate the exact parts of the video where these events occur.


FastPix API allow users to find relevant results to search queries within videos in your platform

How is semantic video search different from regular search?

Traditional video search works by matching keywords in titles or descriptions. But with semantic search, the AI understands context. For example, if you search for "discussion about climate change," it will find relevant scenes, even if they use terms like "global warming" or "environmental changes."

Why is semantic video search helpful?

It saves time: Traditional video search methods often require you to scrub through an entire video just to find a specific 10-second clip. With semantic search, the process becomes effortless. The system understands the meaning behind your query and takes you directly to the relevant part of the video, saving hours of manual effort.

Gives you better search results: Unlike basic keyword searches, semantic video search delves deeper into the context of the content. It matches based on meaning rather than exact terms, making it easier to find what you're looking for—even when different words are used to describe the same concept. This leads to more accurate and relevant search results.

Become more productive: Whether you're a student searching for lecture highlights, a video editor looking for specific clips, or managing a vast content library, semantic search makes your workflow faster and more efficient. By cutting down search time and offering more precise results, it empowers you to focus on what really matters—creating or analyzing content, not hunting for it.

Real-world applications of semantic video search

Semantic video search can be useful across many fields, for now some examples:

Education  
Students and researchers can instantly search through lecture recordings or tutorials to pinpoint specific concepts or topics. This is particularly useful for revisiting key discussions without scrubbing through long videos, making learning and review more efficient.

Marketing & advertising
Marketing teams can swiftly locate relevant video clips to craft personalized campaigns or analyze trends from past content. Instead of manually browsing through extensive video libraries, they can extract the most relevant sections based on the meaning, helping them respond faster to market shifts and customer preferences.

Media & broadcasting
News agencies and broadcasters can quickly retrieve footage that aligns with the context of breaking news stories, allowing them to respond rapidly and accurately. Semantic search allows them to find related clips even if different terminologies or languages are used, improving their ability to create timely, relevant content.

Legal & compliance
Law firms can use semantic video search to scan through hours of surveillance footage or depositions for specific moments that align with legal arguments, saving time and increasing the accuracy of case preparation.

Video production & editing
Video editors can instantly find the precise scenes they need from large video archives based on contextual cues, allowing them to streamline the editing process. This is especially useful in large-scale projects with hundreds of hours of raw footage.

How does it work?

Semantic video search relies on AI and machine learning to understand the content of your video. Here’s a simple breakdown:

Transcript generation

The first step involves transcribing the audio from the video. Advanced speech recognition algorithms listen to the video's audio and convert it into written text. This transcript includes all spoken words and can also capture non-verbal sounds and cues, providing a comprehensive text representation of the video content.

Contextual understanding

Once the transcript is created, the AI applies natural language processing (NLP) and contextual analysis to interpret the text. This involves several sub-steps:

  • Entity recognition: Identifying and categorizing key entities mentioned in the transcript, such as names, locations, and objects.
  • Semantic analysis: Understanding the meanings and relationships between words and phrases. For example, the AI recognizes that "car chase" and "high-speed pursuit" convey similar ideas, even if different terms are used.
  • Contextual mapping: Analyzing the broader context within the video to understand how different pieces of information relate to one another. This helps the AI grasp nuances, such as how "emergency vehicle" might relate to a "police chase."

Search

When you input a search query, the AI uses its understanding of the video content to identify and retrieve relevant segments. This involves:

  • Query interpretation: Parsing the search query to comprehend its intent and meaning. For example, searching for "high-speed chase" will be understood as looking for scenes involving fast-moving vehicles, even if the exact phrase isn't used in the video.
  • Semantic matching: Comparing the meaning of the query with the contextual information from the transcript and video content. The AI finds segments that best match the intended meaning of the query, not just the specific words used.
  • Result ranking: Ranking the retrieved segments based on relevance and context to ensure that the most relevant results are presented first.

Example of semantic video search

Imagine you have a 2-hour documentary about space exploration. If you need to locate a segment where astronauts discuss the moon landing, you could simply enter a query like:

“Conversation about landing on the moon.”

The AI-powered search tool would immediately direct you to the relevant part of the video, even if the astronauts use phrases like “moon landing” or “when we set foot on the moon,” instead of matching your exact words. This flexibility allows you to find specific content based on its meaning, not just the literal text.

Moon landing searched in a video. Results with semantic search


How to implement semantic video search for your content

If you manage a video library for your business, incorporating semantic search tools can significantly enhance the way you find and utilize content:

  1. Upload your videos: Begin by uploading your video files to the semantic search platform.
  2. AI-driven analysis: The platform’s AI will automatically analyze the content, generating transcripts and extracting key details.
  3. Search intuitively: Once the analysis is complete, you can search for specific moments or themes within your videos using natural language, allowing you to find the exact clips you need more efficiently.

Taking video search to the next level

While semantic search offers a significant improvement in video search capabilities, it does have its limitations. Since it's primarily based on transcripts, the search results are restricted to what’s mentioned in the text. For instance, if you search for "cars in a video" or "square objects in the video," semantic search won’t be able to deliver relevant results if these objects aren't explicitly referenced in the transcript.

At FastPix, we push video search beyond these limitations with our In-Video AI. By integrating advanced features like object and logo detection, and text-in-video recognition, we empower your video search functionalities to go beyond words while contextualizing your videos. Our AI not only identifies visual elements but also combines these insights for a deeper, human-like understanding of your videos.

Click here to learn more about FastPix In-Video AI.

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