Computer Vision: Comparison Similarity Images скачать в хорошем качестве

Computer Vision: Comparison Similarity Images

In this video, we will see how comparing image similarity can be considered a numerical representation of the similarity between two images in terms of their visual content. There are several dimensions along which images can be similar, such as color, shape, texture, and composition. Various mathematical and computational methods are used to quantify these similarities, allowing for efficient image comparison and categorization. Main applications of image similarity techniques include e-commerce product comparison, image retrieval, object recognition, and facial recognition. Image similarity, for example, is used in image retrieval to find images similar to a query image. Methods Histogram-based approaches: Histograms capture the distribution of pixel values ​​in an image. By comparing the histograms of two images, their similarity can be measured. Structural similarity approaches: The structural similarity index (SSIM) is a widely used metric that assesses the structural similarity between two images. Consider luminance, contrast, and structure. Feature-based approaches: These methods extract salient features from images, such as edges, corners, or keypoints. Techniques such as Scale-Invariant Feature Transform (SIFT), ORB (Oriented FAST and Rotated BRIEF), and Accelerated Robust Features (SURF) identify distinctive points in images, which can then be compared across images. Deep learning-based approaches: Using pre-trained convolutional neural networks (CNNs) such as ResNet, VGG, and Inception, deep features can be extracted from images. Notebook https://github.com/olonok69/LLM_Noteb...