ImageSimilarity provides Java implementations of algorithms for determining the similarity between images. The project focuses on two core techniques: Perceptual Hash (pHash) and Histogram comparison. pHash generates a unique 'fingerprint' for each image, enabling quick comparison via Hamming distance. Histogram comparison calculates similarity based on the distribution of pixel intensities in images.
The project offers two distinct methods for image similarity measurement, providing flexibility for different use cases. It includes implementations of both perceptual hashing and histogram comparison, which are well-documented and easily configurable. Code examples are provided for both algorithms, facilitating straightforward integration into larger projects.
- Perceptual Hash (pHash): Generates a compact 'fingerprint' for images, enabling fast similarity comparison based on Hamming distance. Simple and efficient, suitable for finding near-duplicate images.
- Histogram Comparison: Calculates image similarity based on histogram data, utilizing the Bhattacharyya coefficient. Effective for gauging overall image similarity.
- Java Implementation: Provides readily usable Java code for both pHash and histogram comparison algorithms, easy to integrate into Java-based projects.
- Clear Documentation: Includes detailed code comments and references to external resources for further learning and understanding.
- Simple to Use: Offers straightforward APIs for calculating image similarity, with minimal dependencies and easy setup.
The project is a functional implementation of image similarity algorithms, with working code for both pHash and histogram comparison. The repository contains code examples and references, although thorough testing and optimization may still be required for production environments. The project is maintained by a single author.
This project benefits developers needing to compare images programmatically in Java. It's valuable for applications like duplicate image detection, image search, and content-based image retrieval. It provides a simple and effective means to assess visual similarity without requiring complex feature extraction.