Shape as Visual Features Containing High-Level Semantic Information

Shape features containing high-level semantic information demonstrate significant application value in content-based image retrieval and image recognition systems. Multiple descriptors exist for characterizing image shapes, among which Fourier descriptors stand out for their ability to simplify two-dimensional contour information into one-dimensional processing problems, making them extensively applicable in computer vision applications. However, natural images typically present noisy and irregular shape characteristics. To address this, we propose an image preprocessing methodology designed to generate purified shape images. Through experimental validation, Fourier descriptor algorithms demonstrate exceptional performance in extracting robust shape features. From an implementation perspective, Fourier descriptors typically involve contour parameterization using boundary coordinates, applying discrete Fourier transform (DFT) to obtain frequency-domain representations, and selecting significant coefficients for dimensionality reduction. The preprocessing pipeline may incorporate noise reduction filters, edge detection algorithms like Canny or Sobel operators, and morphological operations for contour refinement.