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Community Blog Use Open Source PolarDB and imgsmlr for Efficient Image Storage and Similarity Search

Use Open Source PolarDB and imgsmlr for Efficient Image Storage and Similarity Search

This article provides guidance on using open-source PolarDB and imgsmlr for storing image feature values and performing quick image similarity searches.

By digoal

Background

PolarDB's cloud-native architecture offers cost-effective data storage, scalable flexibility, efficient parallel computing capabilities, and fast data search and processing. By combining computing algorithms, PolarDB leverages the value of business data, transforming it into productivity.

This article provides guidance on using open-source PolarDB and imgsmlr for storing image feature values and performing quick image similarity searches.

The test environment for this demonstration is macOS + Docker. For detailed instructions on deploying PolarDB, please refer to the following article:

Simple Deployment of PolarDB

Principle

There are multiple approaches to digitizing an image, such as dividing it into N^2 squares and representing each square with primary colors and gray levels. The image is then progressively reduced layer by layer (e.g., from 81 squares to 9 squares) and compressed into a smaller square forming another N^2 square array.

During an image similarity search, the vector distance between two N^2 square arrays is compared.

By using the GIST index interface, rapid convergence of vector similarity searches can be achieved. This involves data partitioning using the center point as a bucket and employing a multi-layer thumbnail compression search algorithm (refer to the later part of this article).

This article explains how to use open-source PolarDB and imgsmlr to store image feature values and efficiently perform image similarity searches.

1.  Introduce two data types: a detail vector and a signature vector. The signature vector, which occupies less space and offers higher query efficiency, is typically used for initial data filtering, while the detail vector is leveraged for more meticulous filtering.

Datatype Storage length Description
pattern 16388 bytes Result of Haar wavelet transform on the image
signature 64 bytes Short representation of pattern for fast search using GiST indexes

2.  Introduce several image conversion function interfaces.

Function Return type Description
jpeg2pattern(bytea) pattern Convert jpeg image into pattern
png2pattern(bytea) pattern Convert png image into pattern
gif2pattern(bytea) pattern Convert gif image into pattern
pattern2signature(pattern) signature Create signature from pattern
shuffle_pattern(pattern) pattern Shuffle pattern for less sensitivity to image shift

3.  Introduce two vector distance calculation operators and index sorting support.

Operator Left type Right type Return type Description
<-> pattern pattern float8 Eucledian distance between two patterns
<-> signature signature float8 Eucledian distance between two signatures

Deploy imgsmlr on PolarDB

1.  Install the image library dependencies of png and jpeg.

sudo yum install -y libpng-devel  
sudo yum install -y libjpeg-turbo-devel  
  
  
  
sudo vi /etc/ld.so.conf  
# add  
/usr/lib64  
  
sudo ldconfig

2.  Install the gd library for serialization conversion of jpeg, png, gif, and other image formats.

git clone --depth 1 https://github.com/libgd/libgd  
  
cd libgd/  
  
mkdir build  
  
cd build  
  
cmake -DENABLE_PNG=1 -DENABLE_JPEG=1 ..  
  
make  
  
sudo make install  
  
...  
-- Installing: /usr/local/lib64/libgd.so.3.0.16  
-- Installing: /usr/local/lib64/libgd.so.3  
...  
  
  
  
sudo vi /etc/ld.so.conf  
# add  
/usr/local/lib64  
  
sudo ldconfig  
  
  
export LD_LIBRARY_PATH=/usr/local/lib64:$LD_LIBRARY_PATH

3.  Install imgsmlr.

git clone --depth 1 https://github.com/postgrespro/imgsmlr  
  
  
cd imgsmlr/  
USE_PGXS=1 make  
  
USE_PGXS=1 make install  
ldd /home/postgres/tmp_basedir_polardb_pg_1100_bld/lib/imgsmlr.so  
  linux-vdso.so.1 =>  (0x00007ffc25d52000)  
  libgd.so.3 => /usr/local/lib64/libgd.so.3 (0x00007fd7a4463000)  
  libc.so.6 => /lib64/libc.so.6 (0x00007fd7a3ee5000)  
  libstdc++.so.6 => /lib64/libstdc++.so.6 (0x00007fd7a3bdd000)  
  libm.so.6 => /lib64/libm.so.6 (0x00007fd7a38db000)  
  libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007fd7a36c5000)  
  /lib64/ld-linux-x86-64.so.2 (0x00007fd7a42b3000)

4.  Load the plug-in.

psql  
  
create extension imgsmlr ;

Scenario Simulation and Architecture Design Practices

Generate test images.

cd imgsmlr  
USE_PGXS=1 make installcheck

Image import, vectorization, and image similarity search test.

psql
  
  
-- Create a plug-in.
CREATE EXTENSION imgsmlr;  
  
-- Create a table that stores the binary of the original image.
CREATE TABLE image (id integer PRIMARY KEY, data bytea);  
  
-- Create a temporary table for import.
CREATE TABLE tmp (data text);  
  
-- Import images.
\copy tmp from 'data/1.jpg.hex'  
INSERT INTO image VALUES (1, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/2.png.hex'  
INSERT INTO image VALUES (2, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/3.gif.hex'  
INSERT INTO image VALUES (3, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/4.jpg.hex'  
INSERT INTO image VALUES (4, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/5.png.hex'  
INSERT INTO image VALUES (5, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/6.gif.hex'  
INSERT INTO image VALUES (6, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/7.jpg.hex'  
INSERT INTO image VALUES (7, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/8.png.hex'  
INSERT INTO image VALUES (8, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/9.gif.hex'  
INSERT INTO image VALUES (9, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/10.jpg.hex'  
INSERT INTO image VALUES (10, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/11.png.hex'  
INSERT INTO image VALUES (11, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
\copy tmp from 'data/12.gif.hex'  
INSERT INTO image VALUES (12, (SELECT decode(string_agg(data, ''), 'hex') FROM tmp));  
TRUNCATE tmp;  
  
-- Convert the original image into an image feature vector and an image signature, and import it into a new table.
  
CREATE TABLE pat AS (  
    SELECT  
        id,  
        shuffle_pattern(pattern)::text::pattern AS pattern,  
        pattern2signature(pattern)::text::signature AS signature  
    FROM (  
        SELECT   
            id,  
            (CASE WHEN id % 3 = 1 THEN jpeg2pattern(data)  
                  WHEN id % 3 = 2 THEN png2pattern(data)  
                  WHEN id % 3 = 0 THEN gif2pattern(data)  
                  ELSE NULL END) AS pattern   
        FROM   
            image  
    ) x   
);  
  
-- Add a PK.
ALTER TABLE pat ADD PRIMARY KEY (id);  
  
-- Create an index in the image signature field.
ALTER TABLE pat ADD PRIMARY KEY (id);  
  
-- Self-correlate and query image similarity (Euclidean distance).
SELECT p1.id, p2.id, round((p1.pattern <-> p2.pattern)::numeric, 4) FROM pat p1, pat p2 ORDER BY p1.id, p2.id;  
SELECT p1.id, p2.id, round((p1.signature <-> p2.signature)::numeric, 4) FROM pat p1, pat p2 ORDER BY p1.id, p2.id;  
  
  
-- Use the index to quickly search for similar images.
SET enable_seqscan = OFF;  
  
SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 1) LIMIT 3;  
SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 4) LIMIT 3;  
SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 7) LIMIT 3;  
SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 10) LIMIT 3;

Result:

SELECT p1.id, p2.id, round((p1.signature <-> p2.signature)::numeric, 4) FROM pat p1, pat p2 ORDER BY p1.id, p2.id;  
 id | id | round    
----+----+--------  
  1 |  1 | 0.0000  
  1 |  2 | 0.5914  
  1 |  3 | 0.6352  
  1 |  4 | 1.1431  
  1 |  5 | 1.3843  
  1 |  6 | 1.5245  
  1 |  7 | 3.1489  
  1 |  8 | 3.4192  
  1 |  9 | 3.4571  
  1 | 10 | 4.0683  
  1 | 11 | 3.3551  
  1 | 12 | 2.4814  
  2 |  1 | 0.5914  
  2 |  2 | 0.0000  
  2 |  3 | 0.7785  
  2 |  4 | 1.1414  
  2 |  5 | 1.2839  
  2 |  6 | 1.4373  
  2 |  7 | 3.2969  
  2 |  8 | 3.5381  
  2 |  9 | 3.5788  
  2 | 10 | 4.4256  
  2 | 11 | 3.6138  
  2 | 12 | 2.7975  
  3 |  1 | 0.6352  
  3 |  2 | 0.7785  
  3 |  3 | 0.0000  
  3 |  4 | 1.0552  
  3 |  5 | 1.3885  
  3 |  6 | 1.4925  
  3 |  7 | 3.0224  
  3 |  8 | 3.2555  
  3 |  9 | 3.2907  
  3 | 10 | 4.0521  
  3 | 11 | 3.2095  
  3 | 12 | 2.4304  
  4 |  1 | 1.1431  
  4 |  2 | 1.1414  
  4 |  3 | 1.0552  
  4 |  4 | 0.0000  
  4 |  5 | 0.5904  
  4 |  6 | 0.7594  
  4 |  7 | 2.6952  
  4 |  8 | 2.9019  
  4 |  9 | 2.9407  
  4 | 10 | 3.8655  
  4 | 11 | 2.9710  
  4 | 12 | 2.1766  
  5 |  1 | 1.3843  
  5 |  2 | 1.2839  
  5 |  3 | 1.3885  
  5 |  4 | 0.5904  
  5 |  5 | 0.0000  
  5 |  6 | 0.7044  
  5 |  7 | 2.9206  
  5 |  8 | 3.1147  
  5 |  9 | 3.1550  
  5 | 10 | 4.0454  
  5 | 11 | 3.2023  
  5 | 12 | 2.3612  
  6 |  1 | 1.5245  
  6 |  2 | 1.4373  
  6 |  3 | 1.4925  
  6 |  4 | 0.7594  
  6 |  5 | 0.7044  
  6 |  6 | 0.0000  
  6 |  7 | 2.8572  
  6 |  8 | 3.0659  
  6 |  9 | 3.1054  
  6 | 10 | 3.7803  
  6 | 11 | 2.7595  
  6 | 12 | 2.0282  
  7 |  1 | 3.1489  
  7 |  2 | 3.2969  
  7 |  3 | 3.0224  
  7 |  4 | 2.6952  
  7 |  5 | 2.9206  
  7 |  6 | 2.8572  
  7 |  7 | 0.0000  
  7 |  8 | 0.6908  
  7 |  9 | 0.7082  
  7 | 10 | 4.3939  
  7 | 11 | 3.5039  
  7 | 12 | 3.2914  
  8 |  1 | 3.4192  
  8 |  2 | 3.5381  
  8 |  3 | 3.2555  
  8 |  4 | 2.9019  
  8 |  5 | 3.1147  
  8 |  6 | 3.0659  
  8 |  7 | 0.6908  
  8 |  8 | 0.0000  
  8 |  9 | 0.0481  
  8 | 10 | 4.6824  
  8 | 11 | 3.7398  
  8 | 12 | 3.5689  
  9 |  1 | 3.4571  
  9 |  2 | 3.5788  
  9 |  3 | 3.2907  
  9 |  4 | 2.9407  
  9 |  5 | 3.1550  
  9 |  6 | 3.1054  
  9 |  7 | 0.7082  
  9 |  8 | 0.0481  
  9 |  9 | 0.0000  
  9 | 10 | 4.6921  
  9 | 11 | 3.7523  
  9 | 12 | 3.5913  
 10 |  1 | 4.0683  
 10 |  2 | 4.4256  
 10 |  3 | 4.0521  
 10 |  4 | 3.8655  
 10 |  5 | 4.0454  
 10 |  6 | 3.7803  
 10 |  7 | 4.3939  
 10 |  8 | 4.6824  
 10 |  9 | 4.6921  
 10 | 10 | 0.0000  
 10 | 11 | 1.8252  
 10 | 12 | 2.0838  
 11 |  1 | 3.3551  
 11 |  2 | 3.6138  
 11 |  3 | 3.2095  
 11 |  4 | 2.9710  
 11 |  5 | 3.2023  
 11 |  6 | 2.7595  
 11 |  7 | 3.5039  
 11 |  8 | 3.7398  
 11 |  9 | 3.7523  
 11 | 10 | 1.8252  
 11 | 11 | 0.0000  
 11 | 12 | 1.2933  
 12 |  1 | 2.4814  
 12 |  2 | 2.7975  
 12 |  3 | 2.4304  
 12 |  4 | 2.1766  
 12 |  5 | 2.3612  
 12 |  6 | 2.0282  
 12 |  7 | 3.2914  
 12 |  8 | 3.5689  
 12 |  9 | 3.5913  
 12 | 10 | 2.0838  
 12 | 11 | 1.2933  
 12 | 12 | 0.0000  
(144 rows)  
  
  
postgres=# SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 1) LIMIT 3;  
 id   
----  
  1  
  2  
  3  
(3 rows)  
  
postgres=# SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 4) LIMIT 3;  
 id   
----  
  4  
  5  
  6  
(3 rows)  
  
postgres=# SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 7) LIMIT 3;  
 id   
----  
  7  
  8  
  9  
(3 rows)  
  
postgres=# SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 10) LIMIT 3;  
 id   
----  
 10  
 11  
 12  
(3 rows)  
postgres=# explain SELECT id FROM pat ORDER BY signature <-> (SELECT signature FROM pat WHERE id = 10) LIMIT 3;
                                     QUERY PLAN                                     
------------------------------------------------------------------------------------
 Limit  (cost=8.29..10.34 rows=3 width=8)
   InitPlan 1 (returns $0)
     ->  Index Scan using pat_pkey on pat pat_1  (cost=0.14..8.15 rows=1 width=64)
           Index Cond: (id = 10)
   ->  Index Scan using pat_signature_idx on pat  (cost=0.13..8.37 rows=12 width=8)
         Order By: (signature <-> $0)
(6 rows)

Reference

Database Developers Open Source POLARDB Tutorials Similarity Search Image Storage
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