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Machine Vision

Image Similarity Detection Algorithms

By Jan on 02/27/2025

Discover the algorithms used to determine image similarity regardless of size, enabling efficient identification of duplicate or visually alike images.

Image Similarity Detection Algorithms

Table of Contents

Introduction

When dealing with image data, determining if two images are the "same" or at least very similar can be a complex task. Instead of relying on pixel-by-pixel comparisons, which can be sensitive to minor alterations, perceptual hashing algorithms offer a robust solution. These algorithms generate a compact hash value that captures the essence of an image's content, allowing for efficient similarity comparisons.

Step-by-Step Guide

To determine if images are the "same" or similar, you can use perceptual hashing algorithms. These algorithms generate a hash value that represents the image's content, rather than its exact pixel data.

Here's a simplified example of how it works:

  1. Reduce the image size and color depth to simplify the data.

    image = image.resize((8, 8)).convert('L')

  2. Calculate the average color of the simplified image.

    average_color = image.mean()

  3. Compare each pixel to the average color, setting each bit in the hash to 1 if the pixel is brighter and 0 if it's darker.

    hash_value = ''.join(['1' if pixel > average_color else '0' for pixel in image.getdata()])

  4. Compare the hash values of two images. Similar images will have similar hash values, even if they have minor differences.

    from scipy.spatial.distance import hamming

similarity = 1 - hamming(hash1, hash2)

This is a basic example, and there are more sophisticated algorithms like pHash and dHash that offer better accuracy and robustness.

Remember that perceptual hashing is not foolproof and might not detect subtle differences. For exact image comparisons, use byte-by-byte comparison or cryptographic hashing algorithms.

Code Example

The Python code calculates a simple perceptual image hash to compare the similarity of two images. It resizes images, converts them to grayscale, and generates a hash based on pixel brightness relative to the average. The similarity is determined using Hamming distance, with a higher score indicating greater similarity. The code provides an example of comparing two images and interpreting their similarity based on a threshold.

from PIL import Image
from scipy.spatial.distance import hamming

def calculate_hash(image_path):
  """Calculates a simple perceptual hash for an image."""
  image = Image.open(image_path).resize((8, 8)).convert('L')
  average_color = image.mean()
  hash_value = ''.join(['1' if pixel > average_color else '0' for pixel in image.getdata()])
  return hash_value

def compare_hashes(hash1, hash2):
  """Compares two hashes and returns a similarity score."""
  return 1 - hamming(list(hash1), list(hash2))

# Example usage:
image1_path = 'image1.jpg'
image2_path = 'image2.jpg'

hash1 = calculate_hash(image1_path)
hash2 = calculate_hash(image2_path)

similarity = compare_hashes(hash1, hash2)

print(f"Hash 1: {hash1}")
print(f"Hash 2: {hash2}")
print(f"Similarity: {similarity:.2f}")

if similarity >= 0.8:
  print("Images are likely similar.")
else:
  print("Images are likely different.")

Explanation:

  1. calculate_hash(image_path) function:

    • Opens the image from the given path.
    • Resizes it to 8x8 pixels to simplify the data.
    • Converts it to grayscale ('L' mode).
    • Calculates the average color intensity of the image.
    • Generates the hash by comparing each pixel to the average: '1' for brighter, '0' for darker.
    • Returns the hash as a string.

  2. compare_hashes(hash1, hash2) function:

    • Uses hamming distance from scipy.spatial.distance to calculate the difference between the two hashes.
    • Returns a similarity score (1 - Hamming distance), where 1 means identical and 0 means completely different.

  3. Example usage:

    • Sets paths for two images.
    • Calculates hashes for both images.
    • Compares the hashes and prints the similarity score.
    • Provides a basic interpretation of the similarity score.

Remember: This is a simplified example. For more robust and accurate results, consider using established perceptual hashing libraries like ImageHash (which implements pHash, dHash, and others):

pip install ImageHash

Then you can use it like this:

from PIL import Image
from imagehash import phash

image1 = Image.open('image1.jpg')
image2 = Image.open('image2.jpg')

hash1 = phash(image1)
hash2 = phash(image2)

# Calculate the Hamming distance between the hashes
distance = hash1 - hash2

print(f"Hamming distance: {distance}")

Additional Notes

  • Applications: Perceptual hashing is widely used in various applications, including:

    • Duplicate image detection: Identifying and removing duplicate images in large datasets.
    • Content-based image retrieval: Searching for similar images based on visual content rather than metadata.
    • Copyright infringement detection: Finding unauthorized copies or modifications of copyrighted images.
    • Reverse image search: Identifying the source or context of an image.

  • Advantages of Perceptual Hashing:

    • Robustness to minor changes: Tolerant to small variations in images like compression artifacts, resizing, or color adjustments.
    • Compact representation: Hash values are typically much smaller than the original image data, enabling efficient storage and comparison.
    • Fast comparison: Comparing hash values is significantly faster than pixel-by-pixel image comparison.

  • Limitations of Perceptual Hashing:

    • Sensitivity to significant changes: May not perform well when images have undergone substantial modifications like cropping, heavy editing, or object addition/removal.
    • Not suitable for exact matching: Cannot guarantee that two images with identical hash values are pixel-for-pixel the same.
    • False positives/negatives: There's always a possibility of similar images having different hashes or vice-versa.

  • Choosing the Right Algorithm:

    • pHash (Perceptual Hash): Good for robustness against compression and noise.
    • dHash (Difference Hash): Simpler and faster than pHash, but slightly less robust.
    • Average Hash (aHash): The simplest and fastest, but less accurate for subtle differences.

  • Best Practices:

    • Combine with other techniques: For improved accuracy, consider using perceptual hashing alongside other image similarity measures or metadata analysis.
    • Set appropriate similarity thresholds: Experiment with different thresholds to balance precision and recall based on the specific application.
    • Utilize established libraries: Leverage existing libraries like ImageHash for optimized implementations and a wider range of algorithms.

Summary

Feature Description
Purpose Determine if two images are visually similar, even with minor differences.
Method Generate a compact hash value representing the image's content, not exact pixels.
Simplified Steps 1. Reduce image size and color depth.
2. Calculate the average color.
3. Compare each pixel to the average, setting hash bits accordingly.
4. Compare hash values for similarity.
Algorithms Basic example provided, but more sophisticated options like pHash and dHash exist.
Similarity Calculation Hamming distance between hash values, with higher values indicating greater similarity.
Limitations Not foolproof, may miss subtle differences. Not suitable for exact image matching.
Alternatives for Exact Matching Byte-by-byte comparison or cryptographic hashing algorithms.

Conclusion

Perceptual hashing algorithms provide a powerful tool for determining image similarity by generating compact hash values that represent the image's content rather than its exact pixel data. By comparing these hash values, you can efficiently identify similar images even with minor variations. While not foolproof, perceptual hashing offers a robust solution for various applications, including duplicate image detection, content-based retrieval, and copyright infringement detection. Remember to choose the appropriate algorithm (pHash, dHash, aHash) based on your specific needs and consider combining it with other techniques for enhanced accuracy. Utilizing established libraries like ImageHash can further streamline the implementation and improve performance. By understanding the principles and limitations of perceptual hashing, you can effectively leverage its capabilities for a wide range of image-related tasks.

References

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