Image Retrieval Based on Multi-Feature Description and DPF Region Matching

This research presents an image retrieval method based on multi-feature description and DPF region matching, which can effectively improve both accuracy and efficiency in image retrieval tasks. The implementation begins by extracting multiple feature descriptors to characterize different aspects of images, including color histograms (using HSV/YCbCr color spaces), texture patterns (via Gabor filters or LBP algorithms), and shape characteristics (employing Hu moments or Zernike moments). These features are typically computed using OpenCV or MATLAB image processing libraries through functions like cv2.calcHist() for color features and cv2.HuMoments() for shape analysis. Subsequently, the DPF (Dense Pyramid Feature) region matching algorithm compares query images with database images by constructing spatial pyramids and calculating similarity scores while preserving spatial relationships. The algorithm implements multi-scale region comparison through pyramid-level weighting and uses distance metrics like Euclidean or Manhattan distance for similarity computation. Experimental results demonstrate that our proposed method achieves significant performance improvements in image retrieval tasks, effectively enhancing both retrieval precision and computational efficiency through optimized feature fusion and spatial-aware matching mechanisms.