ML Coding - Query Image Similarity Search

Problem Statement

Given a dataset of images, implement a function to perform a query image similarity search. The function should take in a query image and return the most similar image from the dataset.

Constraints

1. The dataset contains a large number of images.
2. The images are represented as vectors in a high-dimensional space.
3. The similarity between images is measured using a distance metric (e.g., Euclidean distance).

Examples

1. Input: Query image vector q, dataset of image vectors D

   • q = [0.1, 0.2, 0.3, ...]
   • D = [[0.4, 0.5, 0.6, ...], [0.7, 0.8, 0.9, ...], ...]
   • Output: The index of the most similar image in D to q.

2. Input: Query image vector q, dataset of image vectors D

   • q = [0.1, 0.2, 0.3, ...]
   • D = [[0.1, 0.2, 0.3, ...], [0.4, 0.5, 0.6, ...], ...]
   • Output: The index of the most similar image in D to q.

Hints

1. Use a distance metric such as Euclidean distance to measure the similarity between images.
2. Consider using a data structure like a k-d tree or a ball tree to efficiently search for the most similar image.

Solution

`python import numpy as np

def euclidean_distance(a, b): return np.sqrt(np.sum((a - b) ** 2))

def query_image_similarity_search(query_image, dataset): min_distance = float('inf') most_similar_index = -1

for i, image in enumerate(dataset):
    distance = euclidean_distance(query_image, image)
    if distance < min_distance:
        min_distance = distance
        most_similar_index = i
        
return most_similar_index

Example usage

query_image = np.array([0.1, 0.2, 0.3]) dataset = [np.array([0.4, 0.5, 0.6]), np.array([0.7, 0.8, 0.9])] print(query_image_similarity_search(query_image, dataset)) `

This solution calculates the Euclidean distance between the query image and each image in the dataset, then returns the index of the image with the smallest distance.

Search Results

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Conclusion

The complete problem statement, examples, constraints, hints, and solution for the "ML Coding - Query Image Similarity Search" question asked at Apple have been provided above.