Visual Search Breakthrough: How Vector Embeddings Are Reshaping Image Search ⭐

Tutorials & Methods
, , ,
1

Visual Search Breakthrough: How Vector Embeddings Are Reshaping Image Search :star:

Imagine trying to find a specific item—say, a vintage-style sofa or a rare bird species—but instead of typing keywords, you simply upload a photo. This is no longer science fiction—image-based search powered by vector embeddings is rapidly transforming how we interact with visual data.

:magnifying_glass_tilted_left: Why Keyword Search Falls Short

Traditional text-based search can’t fully capture the richness of visual content. Keywords struggle to express colors, shapes, textures, or styles. Enter image embeddings—AI-generated numerical representations (aka “fingerprints”) of visual content. These make it possible to measure image similarity using distance metrics in a multi-dimensional space.

:brain: How Vector Search Works

  1. A query image is converted into an embedding (a vector).
  2. The system compares it to other image embeddings in a database.
  3. It calculates the distance between vectors—closer distance means higher visual similarity.
  4. The most similar images are returned—no tags or keywords required.

This method mimics semantic understanding—grouping visually similar images like neighborhoods on a map.

:shopping_bags: Real-World Use Cases

E-Commerce
Upload a picture of a product you like—say, a Scandinavian couch—and get matched with similar products instantly, no keyword guessing needed.

Medical Imaging
Doctors can compare medical scans to databases of known cases, helping diagnose rare conditions or recommend effective treatments.

Content Moderation
AI automatically flags offensive imagery by comparing uploads to a database of prohibited visuals.

Historic Image Retrieval
Search massive archives of untagged photographs by simply uploading similar images—ideal for libraries and researchers.

:light_bulb: Real-Life Demo Using BigQuery

Google BigQuery offers a powerful way to implement image vector search:

  1. Create an embedding model in BigQueryML:
CREATE OR REPLACE MODEL `[PROJECT_ID.DATASET_ID.MODEL_NAME]`
REMOTE WITH CONNECTION `[PROJECT_ID.REGION.CONNECTION_ID]`
OPTIONS (ENDPOINT = 'multimodalembedding@001');
  1. Load and reference image data:
CREATE OR REPLACE EXTERNAL TABLE `[PROJECT_ID.DATASET_ID].external_images_table`
WITH CONNECTION `[PROJECT_ID.REGION.CONNECTION_ID]`
OPTIONS(
  object_metadata = 'SIMPLE',
  uris = ['[BUCKET_NAME]'],
  max_staleness = INTERVAL 1 DAY,
  metadata_cache_mode = 'AUTOMATIC'
);
  1. Generate embeddings for stored images:
CREATE OR REPLACE TABLE `[PROJECT_ID.DATASET_ID].home_embeddings` AS
SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `[PROJECT_ID.DATASET_ID.MODEL_NAME]`,
  TABLE `[PROJECT_ID.DATASET_ID].external_images_table`,
  STRUCT(TRUE AS flatten_json_output, 512 AS output_dimensionality)
);
  1. Generate embeddings for the query image and perform vector search:
SELECT base.uri AS image_link, distance
FROM VECTOR_SEARCH(
  TABLE `[PROJECT_ID.DATASET_ID].home_embeddings`,
  'ml_generate_embedding_result',
  (SELECT * FROM `[PROJECT_ID.REGION].test_embeddings`),
  top_k => 5,
  distance_type => 'COSINE',
  options => '{"use_brute_force":true}'
);

:white_check_mark: The result? The top matches closely resemble the reference image—in this case, a colonial-style house.

:crystal_ball: The Road Ahead

With image embeddings and vector search, we’re stepping into a future where images speak louder than text. This semantics-first approach empowers smarter search, unlocking opportunities in:

  • Retail
  • Healthcare
  • Security
  • Creative discovery

Learn more about image embeddings in BigQuery and Vector Search on BigQuery.

ENJOY & HAPPY LEARNING! :heart:

5 Likes