image similarity

Sohu Tech Products

Sep 13, 2023 · Artificial Intelligence

Implementing Perceptual Hash for Image Similarity Matching in Mini Programs

To automate matching of theme‑image URLs in a mini‑program skin‑changing feature, the author adopts perceptual hashing—using algorithms such as aHash, pHash and dHash—to generate compact 64‑bit fingerprints, compare them via Hamming distance, and reliably identify visually similar images despite minor edits or scaling.

Hamming distancePythonaHash

0 likes · 11 min read

Implementing Perceptual Hash for Image Similarity Matching in Mini Programs

Qunhe Technology Quality Tech

Mar 16, 2023 · Operations

Automating IS Regression Testing with SSIM Image Comparison and Async Rendering

This article describes how the Inspiration Spaces (IS) platform implements an automated regression testing pipeline that uses SSIM image similarity, asynchronous rendering, and pre‑defined sample rooms to dramatically reduce manual effort, improve detection of rendering bugs, and streamline cross‑team collaboration.

AutomationSSIMbackend

0 likes · 11 min read

Automating IS Regression Testing with SSIM Image Comparison and Async Rendering

Kuaishou Tech

Apr 23, 2021 · Backend Development

Automated Randomized Testing Framework for Kuaishou Advertising Landing Pages

This article describes a Selenium‑based automated testing solution that randomly assembles ad landing page components, captures before‑and‑after screenshots, and evaluates visual similarity using histogram and dHash algorithms to ensure "what you see is what you get" across thousands of component combinations.

Seleniumautomated testingimage similarity

0 likes · 14 min read

Automated Randomized Testing Framework for Kuaishou Advertising Landing Pages

360 Quality & Efficiency

Nov 27, 2020 · Artificial Intelligence

Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches

This article reviews common image similarity detection techniques—including hash-based methods (aHash, pHash, dHash), histogram comparison, feature matching with ORB and SIFT/SURF, bag‑of‑words with K‑Means, and CNN‑based VGG16 features—detailing their algorithms, Python implementations, performance characteristics, and practical considerations.

Feature Extractioncomputer visiondeep learning

0 likes · 15 min read

Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches

Xianyu Technology

Mar 19, 2019 · Artificial Intelligence

Page Difference Detection for Automated Regression Testing in Mobile Apps

The paper proposes a method for automated regression testing in mobile apps by detecting page differences via layout segmentation, ORB alignment of scrollable areas, SSIM similarity, and CNN filtering to ignore scroll or cursor changes while highlighting semantic UI changes, demonstrated on the Xianyu app.

CNNORBSSIM

0 likes · 10 min read

Page Difference Detection for Automated Regression Testing in Mobile Apps

System Architect Go

Mar 14, 2019 · Artificial Intelligence

Understanding Image Similarity: Image Hashing and Feature-Based Methods

This article explains why simple MD5 checks cannot assess image similarity and introduces two major approaches—image hashing and image feature extraction—detailing their algorithms, practical performance, and how to compare images efficiently using Hamming distance and indexing techniques.

Feature ExtractionHamming distancecomputer vision

0 likes · 7 min read

Understanding Image Similarity: Image Hashing and Feature-Based Methods

360 Quality & Efficiency

Mar 8, 2019 · Artificial Intelligence

Implementing a Perceptual Hash Algorithm for UI Image Similarity in Android Automation

The article explains how to use a perceptual hash algorithm to generate image fingerprints, compare them to assess UI screen similarity, and integrate this method with UIAutomator for automated decision‑making in the 360 testing project, reducing computational complexity compared to naive double‑loop approaches.

AndroidUI Automationhash algorithm

0 likes · 4 min read

Implementing a Perceptual Hash Algorithm for UI Image Similarity in Android Automation

Xianyu Technology

Apr 26, 2018 · Artificial Intelligence

Client‑Side Image Similarity Computation: Methods, Experiments, and Findings

This study compares hash‑based, CNN‑based, and local‑feature methods for client‑side image similarity detection in e‑commerce, showing that while hash methods are fast and CNNs are accurate but costly, the Hessian‑Affine detector combined with SIFT descriptors delivers the optimal balance of computational efficiency, robustness to transformations, and high recall/precision for on‑device duplicate filtering.

CNNFeature ExtractionMobile Computing

0 likes · 11 min read

Client‑Side Image Similarity Computation: Methods, Experiments, and Findings