Extracting Color Features from Images with Correlation-Based Feature Vectors

The workflow of this method proceeds as follows: First, map the image colors to 8 predefined colors and extract color features. Next, we employ auto-correlation and cross-correlation algorithms to compute the image's feature vector. Implementation typically involves converting images to a standardized color space, applying color quantization using techniques like k-means clustering or predefined color palettes, and calculating correlation matrices to capture spatial color relationships. Finally, we retrieve images from the original database and calculate the accuracy rate of image retrieval using this method. The retrieval process can be implemented using similarity comparison functions (e.g., cosine similarity) between feature vectors. If the image retrieval results meet quality thresholds, they are displayed in a specified folder through automated image export functions. This method finds applications across various domains including image recognition, facial recognition, and security surveillance systems.