Hybrid CNN-ORB Image Feature Extraction and Image Retrieval

Image retrieval systems play a critical role in a variety of applications, from content-based search engines to visual recognition systems. Traditional methods have limitations when it comes to capturing both global and local features of images. To address this, we developed a hybrid image feature extraction system that combines Convolutional Neural Networks (CNNs) for global feature extraction with Oriented FAST and Rotated BRIEF (ORB) for local feature extraction. This hybrid approach aims to enhance the accuracy and relevance of image retrieval results.

Why a Hybrid CNN-ORB Approach?

When searching for similar images, global features such as the overall structure and high-level patterns in an image are crucial, but local details can be equally important in distinguishing between similar images. This hybrid approach seeks to leverage both global and local characteristics:

• CNNs are highly effective at extracting semantic global features that capture high-level attributes like shapes, patterns, and textures. Using pre-trained models such as ResNet, CNNs can identify these features with remarkable precision.

• ORB is a local feature detection and description method that excels at identifying keypoints and distinctive regions in an image, making it useful for detecting finer details that CNNs may overlook.

By combining these two methods, we aim to create a more robust system that provides better image retrieval results by considering both semantic and local features.

Features of the Hybrid Model

1. Global and Local Feature Extraction:

   • ResNet (CNN) is used to extract global features that capture the overall structure of an image.
   • ORB is used to extract local features that capture finer details, making it easier to differentiate between images with similar global structures but different local details.

2. Ranking Mechanism:

   • CNN-based Ranking: Images are ranked based on cosine similarity or Euclidean distance between their global features.
   • ORB-based Ranking: Images are ranked based on Hamming distance between ORB feature descriptors, which compares the local features.

3. Rank Aggregation:

   • The results from both the CNN and ORB methods are combined using a weighted ranking approach to generate a final list of top matches. Initially, equal weights are assigned, but future improvements could involve optimizing the weights based on the dataset.

Tech Stack and Methodology

• Convolutional Neural Networks (CNN): For global feature extraction, we use ResNet, a pre-trained deep learning model available through PyTorch, focusing on features extracted from the penultimate layer.

• ORB (Oriented FAST and Rotated BRIEF): For local feature extraction, we use ORB through OpenCV to detect keypoints and compute descriptors in the images.

• Ranking Functions: Custom functions are implemented to rank the images based on cosine similarity (for CNN features) and Hamming distance (for ORB features).

• Rank Aggregation: A weighted combination of both rankings provides the final retrieval result, aimed at improving accuracy.

How It Works

1. Data Input: The system accepts a query image and searches through a dataset (such as the Paris 6K dataset) to find the most similar images.

2. Feature Extraction:

   • CNN (ResNet) extracts global, high-level semantic features from each image.
   • ORB extracts local features by detecting keypoints and calculating descriptors.

3. Ranking:

   • The system computes similarity scores using CNN for global features and ORB for local features.
   • These scores are combined using a weighted aggregation to produce a ranked list of similar images.

4. Evaluation:

   • The top 3 images are retrieved for each query, and performance is evaluated using metrics like mean average precision (mAP), precision, and recall.

Expected Outcomes

By combining global and local features, we expect the hybrid method to outperform CNN-only or ORB-only methods, particularly in scenarios where local details are important for distinguishing similar images. However, the effectiveness of this approach may vary depending on the dataset, and in some cases, the CNN-only approach might still be sufficient.

Conclusion

The hybrid CNN-ORB approach to image feature extraction provides a promising method for improving the accuracy and relevance of image retrieval systems. By leveraging both global and local features, this approach aims to provide a more nuanced and precise retrieval process, offering significant benefits for applications that require detailed image analysis. The experiments will reveal whether this combination leads to more accurate retrieval results compared to using CNN or ORB features alone.