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.
Zilliz Cloudはどのように画像類似性検索を強化するか
Zillizを利用した画像類似検索システムは、以下のような仕組みになっている：
- 画像は埋め込みモデルを用いてベクトル埋め込みに変換される。
- 画像埋め込みはZilliz Cloudに取り込まれ、保存される。
- ユーザはクエリ画像に類似する画像を検索する。
- 画像クエリは、埋め込みモデルを用いてベクトル埋め込みに変換される。
- クエリの埋め込みは類似検索のためにZilliz Cloudに送られる。
- Zilliz Cloudは近似最近傍(ANN)検索を実行し、クエリと画像ベクトル間の空間距離を比較することで、最も関連性の高いTop-Kの結果を取得します。
- Zilliz CloudはTop-Kの結果をユーザに返します。
成功事例
世界中の業界リーダーがMilvusまたはZilliz Cloud（フルマネージドMilvus）を使用して画像検索システムを構築しています。
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