ImageRetrieval
🥈SilverWith the large-scale image database in the field of science and medicine, as well as in the field of advertising and marketing, it becomes very important to organize the image database and the effective retrieval method. This paper mainly introduces the B/S architecture, focuses on Content-Based Images Retrieval technology, introduces the basic features of image low-level acquisition and corresponding retrieval matching algorithm, including graphics color, local texture and shape characteristics
800Updated 2mo ago
Intermediate30min to implementmarketing
Saves ~5 min per use
Quick InstallView Source
git clone https://github.com/XiaokangLei/ImageRetrieval.gitWorks with:
Claude
Overview
About This Skill
ImageRetrieval is a web-based image search system built on B/S architecture that enables efficient organization and retrieval of large image databases. The system implements content-based image retrieval (CBIR) technology, analyzing low-level image features including color, local texture, and shape characteristics to match and retrieve similar images. Originally developed as an undergraduate capstone project, it addresses the challenge of effectively managing and searching through extensive image collections in scientific, medical, advertising, and marketing domains. The system has been deployed online and is available for demonstration and educational purposes.
How to Use
Access the deployed system at http://39.106.83.234:8080/ImageRetrieval/. The web-based interface allows querying the image database. Full source code and additional documentation are available upon request from the repository contact.
Use Cases
Searching medical image databases by visual similarity
Organizing and retrieving advertising creative assets
Scientific image collection management and discovery
Content-based image browsing without text metadata
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/XiaokangLei/ImageRetrievalRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
I need to retrieve images from a database for [COMPANY]'s [INDUSTRY] campaign. The images should match the following criteria: [DESCRIBE CRITERIA]. Please provide a list of relevant images with their metadata, including color, texture, and shape characteristics.
Example Output
# Image Retrieval Results for [COMPANY]'s [INDUSTRY] Campaign ## Matching Images 1. **Image ID**: IMG_2023_001 - **Description**: A vibrant advertisement featuring a diverse group of people enjoying a product. - **Color**: Dominant hues of blue and green. - **Texture**: Smooth gradients and sharp edges. - **Shape**: Rectangular composition with circular product placement. 2. **Image ID**: IMG_2023_002 - **Description**: A close-up shot of the product with a clean, minimalist background. - **Color**: Predominantly white with accent colors. - **Texture**: Matte finish with subtle reflections. - **Shape**: Symmetrical composition with central focus on the product. 3. **Image ID**: IMG_2023_003 - **Description**: A lifestyle image showing the product in use during an outdoor activity. - **Color**: Earthy tones with bright highlights. - **Texture**: Natural textures and soft focus. - **Shape**: Dynamic composition with diagonal lines. ## Recommendations - **For Print Media**: Use Image ID IMG_2023_001 for its vibrant and engaging visuals. - **For Digital Ads**: Opt for Image ID IMG_2023_002 for its clean and modern aesthetic. - **For Social Media**: Consider Image ID IMG_2023_003 for its relatable and dynamic content.
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