Advanced Image Retrieval Using OpenCV and Computer Vision

Description

This Image Search Engine is a sophisticated Python application leveraging OpenCV and advanced computer vision techniques to enable intelligent image retrieval. By implementing multiple feature extraction methods, the project provides a robust solution for finding visually similar images based on diverse visual characteristics such as color, shape, and texture.

The application empowers users to explore image datasets through intelligent matching algorithms, demonstrating the power of computer vision in solving complex image similarity challenges. By combining multiple feature extraction techniques, the search engine offers a comprehensive approach to visual content analysis.

Feature Extraction Methods

• Color Features:
  • Mean Color: Calculates the average color values across the entire image, providing a simple yet effective color representation.
  • Color Histogram: Analyzes color distribution across different color channels, capturing more nuanced color characteristics.
  • LAB Color Space: Utilizes a perceptually uniform color space for more accurate color similarity measurements.

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• Texture Features:
  • Local Binary Patterns (LBP): Captures local texture patterns by comparing pixel intensities with surrounding pixels, revealing textural characteristics.
  • Haralick Texture Features: Extracts statistical features from the gray-level co-occurrence matrix, providing insights into image texture complexity.

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• Shape Features:
  • Histogram of Oriented Gradients (HOG): Computes gradient directions to capture object shape and edge information.
  • Hu Moments: Provides rotation, scale, and translation-invariant shape descriptors.
  • Zernike Moments: Generates orthogonal moment invariants that effectively describe shape characteristics.