How to Build Image Search with Elasticsearch 8.x and CLIP Multilingual Model

1. What is Image Search?

Image search allows users to upload an image and retrieve similar or related images without typing text, using visual information. It is useful for finding similar images, discovering image sources, or recognizing objects.

The technology relies on image processing and machine learning; deep learning further improves precision.

Examples: Google "Search by Image", Baidu Image Search.

2. Why Use Image Search?

Image search complements text search. Reasons include:

Finding similar images

Discovering image source

Identifying objects in images

Overcoming language and cultural barriers

Example: using Baidu Image Search to identify an insect.

3. How to Implement Image Search with Elasticsearch 8.x

Two core steps: feature extraction and indexing/search.

Step 1: Feature Extraction

Use image processing and machine learning (e.g., CNN) to extract features encoded as vectors. Open‑source libraries include:

OpenCV – C++, Python, Java – provides SIFT, SURF, ORB, etc.

TensorFlow – Python – pretrained models like ResNet, VGG, Inception.

PyTorch – Python – similar pretrained models.

VLFeat – C, MATLAB – algorithms like SIFT, HOG, LBP.

Step 2: Indexing and Search

Store feature vectors in Elasticsearch and use its vector capabilities with script_score or the k‑NN plugin to find similar images.

4. Practical Implementation

4.1 Architecture Overview

Data layer: images collected from the web.

Collection layer: crawlers gather data.

Storage layer: convert images to vectors and store in Elasticsearch.

Business layer: perform k‑NN search on vectors.

4.2 Model Selection

Use sentence‑transformers/clip‑ViT‑B‑32‑multilingual‑v1 , a multilingual version of OpenAI’s CLIP model, to map images and text into a shared dense vector space for image search and multilingual image classification.

Model URL: https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1

4.3 Generating Vectors

Encode images with the model:

model.encode(image)

4.4 Performing Search

Example k‑NN search request:

POST my-image-embeddings/_search
{
  "knn": {
    "field": "image_embedding",
    "k": 5,
    "num_candidates": 10,
    "query_vector": [ ... ]
  },
  "fields": ["image_id", "image_name", "relative_path"]
}

The request uses Elasticsearch’s k‑NN plugin to find the nearest image vectors.

4.5 Result Display

5. Summary

The key components for image search are Elasticsearch and the pretrained sentence‑transformers/clip‑ViT‑B‑32‑multilingual‑v1 model. Feature vectors extracted by the model are stored in Elasticsearch, enabling efficient nearest‑neighbor retrieval when a new image is queried.