Video Similarity Search With Zilliz Cloud Vector Database
Discover videos resembling your video query with Zilliz Cloud-powered video search engines, proven beneficial for video surveillance, deduplication, recommendation, and beyond.
High-Performant Video Search with Unparalleled Precision
Experience a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency. Benefit from unparalleled precision using hybrid vector search with scalar filtering and range search capability.
Efficiently Managing Your Spiking Video Search Data
Say goodbye to data size concerns with Zilliz Cloud. Experience horizontal and independent scaling for computing and storage, aligning with your budget and evolving needs.
Real-Time Responses for Optimized User Experience
Zilliz Cloud guarantees consistently up-to-date search results through real-time data insertion, deletion, and seamless data flows, significantly optimizing the user experience.
Always On, Always Reliable
Zilliz Cloud builds resilient video search systems, guaranteeing uninterrupted service reliability, even in unexpected events through multiple replicas, component isolation, and robust backup and sync capabilities.
Simple Start, Amplified Productivity
Deploy a large-scale similarity search service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive user experience.
질리즈 클라우드가 동영상 유사도 검색을 지원하는 방법
질리즈가 제공하는 동영상 유사도 검색 시스템은 다음과 같은 방식으로 작동합니다:
- 동영상은 임베딩 모델을 사용하여 벡터 임베딩으로 변환됩니다.
- 동영상 임베딩이 수집되어 질리즈 클라우드에 저장됩니다.
- 사용자가 자신의 동영상과 유사한 동영상을 쿼리합니다.
- 동영상 쿼리는 임베딩 모델을 사용하여 벡터 임베딩으로 변환됩니다.
- 쿼리 임베딩은 유사도 검색을 위해 질리즈 클라우드로 전송됩니다.
- 질리즈 클라우드는 ANN 검색을 수행하여 가장 연관성이 높은 Top-K 결과를 검색합니다.
- 질리즈 클라우드는 Top-K 결과를 사용자에게 반환합니다.
성공 사례
전 세계 업계 리더들은 Milvus 또는 Zilliz Cloud(완전 관리형 Milvus)를 사용하여 동영상 검색 비즈니스를 강화합니다.
> 100M
embedding vector storage and searching
Seamlessly integration
with various internal systems and tech stacks
Enhanced real-time data retrieval
with reduced latency and increased system availability
Entertainment
How Milvus Transformed BIGO's Video Deduplication System for Optimal Throughput and User Experience
지금 읽기<200ms
search response time with a high recall rate
>700 million
embedding vectors indexing and management
Significantly increased
query throughput without compromising performance
> 100M
embedding vector storage and searching
Seamlessly integration
with various internal systems and tech stacks
Enhanced real-time data retrieval
with reduced latency and increased system availability
Entertainment
How Milvus Transformed BIGO's Video Deduplication System for Optimal Throughput and User Experience
지금 읽기<200ms
search response time with a high recall rate
>700 million
embedding vectors indexing and management
Significantly increased
query throughput without compromising performance