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Object Retrieval System for Image

This repository contains an implementation of an image retrieval system designed to search for and rank similar object in images from a large gallery based on query images. The system leverages CNN-based feature extraction to enhance retrieval performance. It is built to process a gallery of images and return ranked results based on similarity to a given query.

Example

Retrieving an object from other images

Features

  1. A CNN-based approach using a pre-trained ResNet50 model for feature extraction, paired with cosine similarity for ranking.
  • Modular Design: Code is organized into separate folders for each method, making it easy to extend or modify.

Repository Structure

  • extract_feature_multi_process.py: Feature extraction using ResNet50.
  • all_retrieval.py: Image retrieval and ranking using cosine similarity.

Methodology

Method 1: CNN-Based Retrieval (ResNet50)

This method extracts deep features from images using a pre-trained ResNet50 model and ranks gallery images based on cosine similarity.

  • Feature Extraction:
    • Images are pre-processed (BGR to RGB conversion) using OpenCV.
    • Objects are cropped using a Fast R-CNN model when multiple instances are present.
    • ResNet50 extracts features after removing the fully connected layer, followed by global average pooling to generate a 1D feature vector.
  • Retrieval:
    • Cosine similarity is computed between the query feature vector and gallery feature vectors.
    • Top 10 images with the highest similarity scores are returned.

Setup and Usage

Prerequisites

  • Python 3.x
  • Libraries: OpenCV, NumPy, PyTorch (for ResNet50), and other dependencies (see requirements.txt if provided).
  • A gallery of images (not included in this repo).

Installation

  1. Clone the repository: 
    git clone https://github.com/yourusername/image-retrieval-system.git
cd image-retrieval-system
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Add your gallery images to the gallery/ folder and query images to the query/ folder.

Running the Code

  • For CNN-based retrieval: 
    python /extract_feature_multi_process.py
python /all_retrieval.py

Notes

  • The gallery/ folder is empty to save space. Populate it with your image dataset before running the code.
  • Query images and bounding box annotations (if required) should be placed in the query/ folder.
  • Adjust file paths in the scripts if your dataset structure differs.