Extracting GIST Vectors from Images

This article introduces an image processing method that utilizes GIST vector extraction technology and compares these vectors through Euclidean distance calculations. Widely applied in computer vision, particularly in image recognition and classification tasks, this approach converts images into feature vectors to better understand and compare their similarities and differences. Implementation typically involves using MATLAB's LMgist descriptor or OpenCV's feature extraction functions to compute GIST vectors that capture spatial envelope properties. The Euclidean distance calculation between vectors can be implemented using NumPy's linalg.norm function or custom distance computation algorithms. By employing this method, we can achieve more accurate image identification and classification while providing valuable information for other computer vision applications. The process generally follows these computational steps: image preprocessing, GIST descriptor extraction using oriented filters, vector normalization, and distance matrix computation for similarity assessment.