Image Similarity Search with Zilliz Cloud Vector Database
Discover visually similar images effortlessly with Zilliz Cloud-powered image similarity search engines, proving valuable in various fields like e-commerce and art.
Effortlessly Accommodate Your Spiking Image 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. Manage billions of image vectors within hours with efficient bulk inserts.
High-Performant Image Similarity 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.
Real-Time Image Search Results 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 image search systems, guaranteeing uninterrupted service reliability even in unexpected events through multiple replicas, component isolation, and robust backup and sync capabilities.
Start with Simplicity and Amplified Productivity
Deploy a large-scale image similarity search service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive user experience.
질리즈 클라우드가 이미지 유사도 검색을 지원하는 방법
Zilliz 기반 이미지 유사도 검색 시스템은 다음과 같은 방식으로 작동합니다:
- 임베딩 모델을 사용하여 이미지를 벡터 임베딩으로 변환합니다.
- 이미지 임베딩은 수집되어 질리즈 클라우드에 저장됩니다.
- 사용자는 쿼리 이미지와 유사한 이미지를 검색합니다.
- 이미지 쿼리는 임베딩 모델을 사용하여 벡터 임베딩으로 변환됩니다.
- 쿼리 임베딩은 유사도 검색을 위해 질리즈 클라우드로 전송됩니다.
- 질리즈 클라우드는 근사 근접 이웃(ANN) 검색을 수행하여 쿼리와 이미지 벡터 사이의 공간 거리를 비교하여 가장 연관성이 높은 Top-K 결과를 검색합니다.
- 질리즈 클라우드는 Top-K 결과를 사용자에게 반환합니다.
성공 사례
전 세계 업계 리더들은 이미지 검색 시스템을 구축하기 위해 Milvus 또는 Zilliz Cloud(완전 관리형 Milvus)를 사용합니다.
90% reduction
in search latency
Sharp reduction
on $ and time
Easily scale
as business grows
Handled tens of millions of
vector embeddings with fast scaling capability
Achieved millisecond-level
query speed with optimal user experience
Reshaped the future of fashion
with a harmonious fusion of human creativity and machine intelligence
90% reduction
in search latency
Sharp reduction
on $ and time
Easily scale
as business grows
Handled tens of millions of
vector embeddings with fast scaling capability
Achieved millisecond-level
query speed with optimal user experience
Reshaped the future of fashion
with a harmonious fusion of human creativity and machine intelligence