Pesquisa de semelhança de vídeo
Potenciar o seu sistema de pesquisa de similaridade de vídeo com a base de dados vetorial Zilliz Cloud (totalmente gerida por Milvus)
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.
Como é que o Zilliz Cloud potencia a pesquisa de semelhança de vídeo
Um sistema de pesquisa por semelhança de vídeo alimentado pelo Zilliz funciona da seguinte forma:
- Os vídeos são transformados em vectores de incorporação utilizando um modelo de incorporação.
- Os embeddings de vídeo são ingeridos e armazenados no Zilliz Cloud.
- Um utilizador procura vídeos semelhantes ao seu vídeo.
- A consulta de vídeo é transformada em embeddings vectoriais utilizando um modelo de embedding.
- Os embeddings da consulta são enviados para o Zilliz Cloud para uma pesquisa de semelhanças.
- O Zilliz Cloud efectua pesquisas ANN e recupera os K melhores resultados mais relevantes.
- O Zilliz Cloud devolve os K melhores resultados ao utilizador.
Histórias de Sucesso
Os líderes da indústria em todo o mundo utilizam o Milvus ou o Zilliz Cloud (o Milvus totalmente gerido) para impulsionar o seu negócio de pesquisa de vídeo.
> 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
Leia agora<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
Leia agora<200ms
search response time with a high recall rate
>700 million
embedding vectors indexing and management
Significantly increased
query throughput without compromising performance