Milvus is an open-source vector database designed for efficient similarity search across large datasets, particularly excelling in high-dimensional vectors like those representing images.
Instead of storing raw images, it uses pre-trained deep learning models to extract feature vectors that encapsulate visual content.
The image retrieval architecture involves processing user queries, converting images into embeddings, querying against the Milvus database, and presenting similar images. The process includes data preparation, importing necessary packages, feature extraction using pre-trained models, building a search pipeline involving creating a Milvus collection, inserting data, and executing search queries.
Milvus offers scalability, performance, and flexibility in handling massive image collections, enabling real-time retrieval with customizable search options. It serves as a robust foundation for building image retrieval systems for various applications.
Detailed information on Milvus integration with watsonx.data for image retrieval usecase using python SDK is available at https://medium.com/ibm-data-ai/image-retrieval-with-ibm-watsonx-data-f4bdd1ec1824
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