Four Algorithms for Image Feature Extraction

In this article, I would like to provide a detailed introduction to four algorithms for image feature extraction. These algorithms were obtained through considerable effort, but I believe sharing them is highly valuable. First, let's discuss the first algorithm, which is a feature extraction method based on color histograms. This approach analyzes the color distribution within an image to extract features, typically implemented using histogram calculation functions that quantify pixel intensity distributions across color channels. Next, we will explore the second algorithm, a feature extraction method based on edge detection. This technique detects edges in images to extract features, often employing operators like Sobel, Canny, or Prewitt that compute gradients to identify rapid intensity changes. The third algorithm is a feature extraction method based on texture analysis. It analyzes texture patterns in images to extract features, commonly using methods such as Local Binary Patterns (LBP), Gray-Level Co-occurrence Matrix (GLCM), or Gabor filters that capture spatial relationships and repetitive patterns. Finally, we will introduce the fourth algorithm, a feature extraction method based on shape descriptors. This approach describes the shapes of objects in images to extract features, frequently utilizing techniques like Histogram of Oriented Gradients (HOG), Fourier descriptors, or moment invariants that capture geometric properties and contour characteristics. I hope these detailed explanations help everyone better understand image feature extraction algorithms. If you have any questions, please feel free to ask!